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What is Bias?


We all know that society is biased for ages. Bias based on gender, race, age, socioeconomic status, etc have consciously or unconsciously been part of human thoughts and actions. In this modern society with the help of raising awareness, most of us are coming forward to fight against discrimination and prejudice that is affecting human decision-making.

But what about the decision-making done by intelligent systems and applications that are increasingly becoming an inevitable part of our lives?

These intelligent applications are built on data supplied by humans. When the bias is present in human thoughts and actions, there is no surprise that the intelligent applications that we are developing are inheriting this bias from us.

What is Data Bias?

Consider an NLP application fills a sentence as ‘Father is to doctor as a mother is to nurse’.

The above NLP example is directly linked to the gender inequality present in society.

Consider more examples:

Why did one of the popular AI-based recruitment software biased against women applicants?

Why did Siri and Alexa show gender bias initially?

There are many reports that a lot of image processing applications fail to recognize women, especially dark-skinned women.

Why did the AI-based decision support application fail to identify criminals belonging to a particular race?

Why is this bias in the output given by these ML/AI applications?

Because the Machine Learning / AI applications that we design learn from the data that we feed to them. The data we feed contains the prejudices and inequalities that exist in the human world consciously or unconsciously.

  How serious are the implications of neglecting bias in the data?

As Data Scientists/ Data Analysts/Machine Learning Engineers and AI practitioners, we know that if our data sample does not represent the whole population, then our results are not statistically significant. Which means that we do not get accurate results.

Machine Learning models built on such data would perform worse on underrepresented data.

Consider an example from one of the critical domains, Healthcare, where data bias would result in devastating results.

The AI algorithms developed to detect skin cancer as perfectly as an experienced dermatologist failed to detect skin cancers in people with dark skin. Refer to the picture shown above.

Why did this happen?

Because the dataset was imbalanced. Majority of the images on which the algorithms were trained belong to light-skinned individuals. The data that was used to train these algorithms was taken from those states where the majority are white-skinned people. Hence, the algorithm fails to detect the disease in dark-skinned people when the images belonging to them were given to it.

Another AI application developed to identify the early stages of Alzheimer’s disease in people took auditory tests from people. It takes the way a person speaks as input and analyzes that data to identify the disease. But as the algorithm was trained on the data from Native English speakers, when a non-native English speaker takes the test, it wrongly identified the pauses and mispronunciations as indicators of the disease. (An example of false positive)

What are the consequences of this wrong diagnosis in the above two examples? Where in the development process have we gone wrong? How can AI bias occur?

There are multiple factors behind these AI biases. There is no single root cause.

1. Missing diverse demographic categories.

Sampling errors are also majorly the result of improper data collection methods.

Datasets that do not include diverse demographic categories will be imbalanced/skewed and there are higher chances of overlooking these factors during the data cleaning phase.

2. Bias inherited from humans.

As discussed above, bias can be induced into data while labeling, most of the time unintentionally, by humans in supervised learning. This can be due to the fact that unconscious bias is present in humans. As this data teaches and trains the AI algorithm on how to analyze and give predictions, the output will have anomalies.

3. During the feature engineering phase

During the feature engineering phase, bias can occur.

For example, while developing an ML application for predicting loan approval, if features like race, gender are considered, these features would induce bias.

On the contrary, while developing an AI application for healthcare, if the same features like race, gender are removed from the dataset, this would result in the errors explained in the healthcare examples above.

Research on handling AI Bias

AI is being used widely in not only the popular domains but also in very sensitive domains like health care, criminal justice, etc. Hence, the debate on biased data and fairness in the output is always on in data and AI communities.

There is so much research and study going on to identify how bias is induced into the AI systems and how to handle it to reduce errors. Responsible AI and ethical AI are also been adopted widely to tackle the problem of bias too along with other AI challenges.

Are we not responsible to reduce this data bias?

One of the primary goals of using AI in decision support systems should be to make decisions less biased than humans.

Should we leave this biased-data problem to the researchers and carry on with our regular data cleaning tasks and trying to improve the accuracy of our algorithms as part of our development work?

As Artificial Intelligence is growing deeper and deeper into our lives, bias in data that is used for developing these applications can have serious implications not only on human life but also on the entire planet.

Hence, it is everyone’s responsibility to work towards identifying and handling bias at the early stages of development.

What is our part to reduce data bias?

Every data Machine Learning engineer/AI practitioner has to take the responsibility of identifying and removing bias while he works on developing artificially intelligent applications.

Here are some of the steps we can consider to take this forward.

We should not blindly build, develop applications with whatever data is available to us.

We need to work with researchers too and ensure that diverse data is available for our model development.

We have to be careful during the data collection phase to gain enough domain knowledge on the problem we are working on to be able to assess if the data collected includes diverse factors and has any chances of bias.

During the feature engineering phase, we should study the features in-depth combined with more research on the problem domain we are working in, to eliminate any features that may possibly induce bias.

Explainable AI and Interpretable AI also helps us to build trust in algorithms by ensuring fairness, inclusivity, transparency, and reliability.

Testing and evaluating the models carefully by measuring accuracy levels for different demographic categories and sensitive groups may also help in reducing algorithmic bias.

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Prasuna Atreyapurapu:

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What Is Perception Bias?

Perception bias is the tendency to perceive ourselves and our environment in a subjective way. Although we like to think our judgment is impartial, we are, in fact, unconsciously influenced by our assumptions and expectations.

Example: Perception biasAfter a few weeks at your new job, you notice that some of your colleagues always go for after-work drinks on Fridays. It’s not an official team event, but each week the same person asks who’s joining and books a table. However, no one ever asks the older colleagues to join, assuming that they won’t be interested.

If left unchecked, perception bias can affect how we evaluate ourselves and others. As a result, we may form inaccurate impressions.This, in turn, can impact the quality of our decision-making.

What is perception bias?

Perception bias is a broad term used to describe different situations in which we perceive inaccuracies in our environment. It is a type of cognitive bias that occurs when we subconsciously form assumptions or draw conclusions based on our beliefs, expectations, or emotions.

Perception bias works like a filter, helping us make sense of all the information we are exposed to in our surroundings. As a result, our perception of reality is often distorted. For example, this can cause us to unfairly label people or make inferences about their abilities on the basis of superficial observations or stereotypes.

Why does perception bias occur?

Perception bias occurs because our perception is selective. Here, perception refers to the process of screening, selecting, organizing, and interpreting stimuli, such as words or objects, in order to give them meaning. Our brain chooses to hone in on one or very few stimuli out of the multitude of stimuli surrounding us. This is one way our brains differentiate between important and unimportant things.

Due to this, our perception of a given situation is not a photographic representation of reality. Rather, it is a unique representation, informed by objective information, our prior beliefs and expectations (called cognitive factors), and our hopes, desires, and emotions (called motivational factors). Motivational and cognitive factors are sometimes intertwined, but they can also function separately.

What are different types of perception bias?

There are many types of bias that can influence our perceptions, whether of objects, others, or ourselves. Although there is no exhaustive list, the following are some common types of perception bias:

Visual Perception. When we look at something, our brains use the information available (like visual cues or prior experience) to make sense of an object. This means that our visual processing of faces can be biased. For example, a person’s group membership may lead us to view their face as untrustworthy. Negative attitudes and beliefs like outgroup bias can have an effect on our visual perception.

Self-perception. People are often biased in their self-perceptions, failing to assess themselves accurately. For example, people may take personal responsibility for successes while denying personal responsibility for failures (self-serving bias), or they may underestimate their performance and abilities, casting themselves in a more negative light (self-effacement bias). When comparing the self to others, people often commit what is known as the false consensus effect, believing that our opinions or behaviors are generalizable to the general population.

Perception bias examples

Example: Perception bias in the workplaceYou are the lead for an important project at work, and your manager asks you to present your progress to the executive board. You spend hours preparing the presentation with your team. After the presentation, your manager congratulates you for your progress and the presentation. In reply, you say “I worked really hard on this,” happy to take all the credit. Since you are the project lead, you believe this praise is fair. However, this is not entirely accurate because you worked as a team. This is an example of a type of perception bias called self-enhancement.

Selective perception bias can help explain why individuals with opposing views tend to find the same media coverage to be biased against them.

Example: Selective perception bias and the “hostile media effect”In one study, researchers took a sample of pro-Israeli, pro-Arab, and neutral college students. They asked them to watch the same set of televised news segments covering the Arab-Israeli conflict, broadcast nationally in the United States over a ten-day period.

Researchers found that each side saw the news coverage as biased in favor of the other side.

Pro-Arab students thought the news segments were generally biased in favor of Israel.

Pro-Israeli students thought the segments were generally biased against Israel.

Neutral students gave opinions that fell between the two groups.

They also found that these disagreements were not simply differences of opinion; they were differences in perception. In particular, pro-Arab and pro-Israeli students also differed in their perceptions of the number of favorable and unfavorable references that had been made about Israel during the news program.

Pro-Arab students reported that 42 percent of the references to Israel had been favorable, and only 26 percent had been unfavorable.

Pro-Israeli students recalled 57 percent of the references to Israel as having been unfavorable and only 16 percent as having been favorable.

The researchers concluded that individuals with strong preexisting attitudes on an issue perceive media coverage as unfairly biased against their side and in favor of their opponents’ point of view. This happens because when people become committed to a particular cause or opinion, their perceptions often change in order to remain consistent with this commitment.

How to reduce perception bias

Although it is not possible to entirely eliminate perception bias, there are ways to reduce it. More specifically, when you make a decision or form an impression of someone, you can ask yourself the following questions:

Do I have a motive that makes me see things a certain way?

What are my expectations from this situation or decision?

Have I discussed my thoughts or opinions with people who don’t agree with me?

If you find yourself making absolute statements about others, using strong words like “always” or “all,” ask yourself how accurate this is, and whether you have evidence to back up your claim.

Other types of research bias Frequently asked questions about perception bias

What is an everyday life example of perception bias?

A real-life example of perception bias is the false consensus effect. Because we spend most of our time with friends, family, and colleagues who share the same opinions or values we do, we are often misled to believe that the majority of people think or act in ways similar to us. This explains, for instance, why some people take office supplies home: they may genuinely feel that this behavior is more common than it really is.

Why is perception bias a problem?

Perception bias is a problem because it prevents us from seeing situations or people objectively. Rather, our expectations, beliefs, or emotions interfere with how we interpret reality. This, in turn, can cause us to misjudge ourselves or others. For example, our prejudices can interfere with whether we perceive people’s faces as friendly or unfriendly.

What is selective perception?

Selective perception is the unconscious process by which people screen, select, and notice objects in their environment. During this process, information tends to be selectively perceived in ways that align with existing attitudes, beliefs, and goals.

Although this allows us to concentrate only on the information that is relevant for us at present, it can also lead to perception bias. For example, while driving, if you become hyper-focused on reaching your exit on a highway, your brain may filter visual stimuli so that you can only focus on things you need to notice in order to exit the highway. However, this can also cause you to miss other things happening around you on the road.

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What Is Optimism Bias?

Optimism bias is the tendency to overestimate the likelihood of positive events and underestimate the likelihood of negative events. Optimism bias causes most people to expect that things will work out well, even if rationality suggests that problems are inevitable in life.

Example: Optimism biasYou’ve just bought a new bike, and the salesperson asks you whether you also want to look for a helmet.

Because you’ve been riding a bike since you were young, you think the chances of getting involved in an accident are really small. You conclude that you’ll be fine without it. Optimism bias makes you underestimate the risk of riding a bike without a helmet.

Although optimism bias can motivate us to overcome obstacles, it can also cause us to ignore potential risks, resulting in poor decision-making.

What is optimism bias?

Optimism bias (or unrealistic optimism) is a type of unconscious cognitive bias. It refers to an unrealistically favorable attitude that people have towards themselves and people that are close to them. Positive illusions help us maintain self-esteem and avoid discomfort, at least in the short term.

Optimism bias causes people to believe that they are less likely to experience negative events than other people. For example, people expect that their careers, marriages, or health will be better than those of others, and that the financial troubles, divorces, or illnesses that happen to other people will not happen to them.

This irrational belief seems to be deeply ingrained in humans. Studies suggest that it is observed in about 80% of the population (but, notably, not among people with depression).

Why does optimism bias occur?

Throughout human evolution this characteristic served us well and was passed down from one generation to the next. In other words, because optimism bias proved beneficial to humans, we are inclined to mispredict the future.

There are two key assumptions at the root of optimism bias:

That we exercise some level of control over the world around us, including what will happen to us in the future.

That we, as individuals, possess more positive traits than the average person.

Several factors can help explain optimism bias:

We have the tendency to selectively update our beliefs and expectations about the future. We are more likely to update our beliefs based on positive information rather than negative information. This, in turn, perpetuates optimism bias.

Optimism is beneficial to our mental and physical health. Expecting positive outcomes reduces stress and anxiety. Optimistic patients are more likely to believe that they will recover, leading them to adopt behaviours that increase their chances (e.g., exercise, healthy diet).

Overall, optimism bias enables us to cope with our environment and worry less about uncertainty. Because of this, it can often lead to better results than unbiased or rational beliefs.

Why does optimism bias matter?

Because a majority of people are susceptible to optimism bias, it’s important to be aware of its influence on our perception and judgment.

Optimism bias can be a problem when it prevents us from accurately anticipating risk. In project management, for instance, optimism bias can cause us to underestimate the budget and time needed, a common error called the planning fallacy. Failure to assess potential hazards can also mean failing to take out sufficient insurance or to get regular medical check-ups. It can even cause us to adopt harmful habits, such as smoking.

On the other hand, optimism is also linked to achievement in several domains, such as sports, business, and education. When we are optimistic, we are more motivated to try harder, which in turn can influence the outcome. Sometimes, expecting positive things can become a self-fulfilling prophecy.

Optimism bias examples

Optimism bias can also influence collective behaviour and produce large-scale effects.

Example: Optimism bias and the economySeveral experts consider optimism bias to be one of the core causes of the financial crisis of 2007-2008. Individuals, analysts, and government officials were all too optimistic that the economy would grow (i.e., that businesses would continue to be profitable, that there would be more jobs for people, and that incomes would increase), leading them to ignore any warning signs.

This shows that when many people hold unrealistic expectations, their bias accumulates and is amplified, producing large scale effects.

Optimism bias can have negative consequences, particularly when serious risks are disregarded.

Example: Optimism bias and climate changeSome argue that optimism bias may help explain why we don’t do anything about climate change, even though we acknowledge the threat. Studies have shown that people who are more optimistic about a range of possible future events (e.g., contracting an illness, World War III) are also less concerned about the environment.

Additionally, among climate skeptics in particular, more optimism is associated with less guilt, less perceived responsibility, and lower behavioural intentions. Thus, overall, optimism seems to be negatively associated with an active response to environmental change.

How to avoid optimism bias

Although optimism bias is part of human nature (and can’t be entirely avoided), there are ways to keep it in check:

Perform a project “premortem.” A premortem analysis starts with the hypothesis that your project has failed. With that in mind, you try to come up with possible reasons why. This allows you to spot the weaknesses in your project plan and prepare for the future.

Use the availability heuristic. Actively attempt to retrieve negative past experiences or times things didn’t go as planned. Here, the purpose is not to demotivate yourself, but to learn from the past so as to make sensible choices in the future.

Take an outsider’s approach. Take an objective approach when making plans. For example, when you estimate how long you will need to write a paper, seek out information about the average time it takes most people and adjust your initial assumptions accordingly.

Other types of research bias Frequently asked questions about optimism bias

What is the opposite of optimism bias?

The opposite of optimism bias is pessimism bias. Optimism bias occurs when we overestimate our chances of experiencing positive events in our lives, while pessimism bias occurs when we overestimate our chance of experiencing negative events.

For example, pessimism bias could cause someone to think they are going to fail an exam, even though they are well prepared and usually get good grades.

What is a positive illusion?

A positive illusion is a form of self-deception under which people have inflated, favorable attitudes about themselves or others close to them.

The most common positive illusions involve:

Exaggerating one’s positive traits

Overestimating one’s degree of control in life

Harboring overly optimistic beliefs about future events (also called optimism bias).

What is the planning fallacy?

The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.

For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias.

What is a self-fulfilling prophecy?

This suggests that beliefs have the power to alter people’s behavior in such a way that they become a new reality in the end. Optimism bias can create a self-fulfilling prophecy: when we expect positive things, we are more likely to align our actions with this belief and try harder to influence the outcome.

What is positivity bias?

Positivity bias occurs when a person judges individual members of a group positively, even when they have negative impressions or judgments of the group as a whole. Positivity bias is closely related to optimism bias, or the expectation that things will work out well, even if rationality suggests that problems are inevitable in life.

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Test Bias With Minority Group

Psychologists use testing and assessment in a variety of contexts for a variety of purposes, including but not limited to job placement, diagnosing psychological disorders for mental health treatment, verifying health insurance coverage, conducting focus groups for market research, informing legal decisions and government policies, and developing measures to reliably assess personality characteristics.

The American Psychological Association’s (APA) Ethical Principles of Psychologists and Code of Conduct (2002) and the Standards for Education and Psychological Testing give guidelines for the ethical conduct of psychological testing and evaluation with racial/ethnic minorities.

Test Bias Types of Test Biases

following are the general categories of test bias

Construct- Validity Bias − This refers to whether a test directly measures what it was designed to measure. On an intelligence test, for illustration, scholars who are learning English will probably encounter words they have yet to learn. Accordingly, test results may reflect their fairly weak English- language chops rather than their academic or intellectual capacities.

Content- Validity Bias − This bias occurs when the content of a test is comparatively more delicate for one group of scholars than for others. For example, it can do when members of a pupil group, similar to various young groups, have not been given the same occasion to learn the material being tested, when scoring is illegal to a group (for illustration, the answers that would make sense in one group’s culture are supposed incorrect), or when questions are articulated in ways that are strange to certain scholars because of verbal or artistic differences. Item-selection bias, a subcategory of this bias, refers to individual test particulars more suited to one group’s language and artistic behaviors.

Test Bias with a Minority Group

The assessment process involving ethnical minorities has numerous avenues by which bias can crop. The impulses can do because of differences in culture or race and minority group status. Although culture has been defined in numerous ways, it generally refers to the behavior patterns, symbols, institutions, values, and mortal products of a society. On the other hand, race can be used to describe a racial, public, or artistic group. One’s race generally conveys a social-cerebral sense of “peoplehood” in which group members share a social and artistic heritage transmitted from one generation to another.

Moreover, racial group members frequently feel an interdependence of fate with others in the group. In addition to culture and race, members of racial minority groups also witness minority group status that involves a history of race or racial relations. This history has affected interpersonal relations, prospects, and performances. Therefore, to completely understand racial minority groups, their responses, and the assessment process, culture, race, and minority group status must be anatomized. Concern with test and dimension bias is not simply a matter of being “politically correct” or eternalized by ethnics disgruntled by their issues on colorful tests and measures. Bias does live in numerous of our assessment instruments and procedures.

Cultural bias in testing occurs if an assessment unfairly measures scholars’ skills and knowledge without considering scholars’ understanding of artistic traditions. When assessments do not consider scholars’ artistic differences, they fail to measure scholars’ capacities directly and can lead to opinions grounded on inaccurate data. Cultural bias in testing can occur when the annotator or the testing accouterments do not consider scholars’ lack of knowledge of semantics and experiences within a particular artistic group.

The Impact of cultural bias in testing is that a disproportionate number of scholars from minority artistic backgrounds have appertained to special education services. Also, when measuring proficiency in a language, scholars can be inaptly labeled as impaired because the test results indicate a language impairment. Still, the distinction in data may be due to artistic differences. The main specific of artistic test bias is that the tests are made up of a homogenous group of people who do not represent the cultural diversity of the scholars who take the test. In addition, the test itself could be culturally prejudiced because of the content of test particulars, the formatting of the test, or the terrain in which the assessment is being given.

One effect of cultural bias in testing is maintaining ethical conceptions by unfairly representing data as a suggestion of intelligence or capability. As a result, testing results unfairly measure scholars of color, scoring lower when the fault lies in prejudiced testing, not furnishing accurate measures of scholars’ capacities. As a result, scholars of color are placed in special education programs at a disproportionate rate. Likewise, prejudiced standardized testing perpetuates misconceptions about marginalized people and good academic achievement prospects.

Steps to Reduce Test Biases

Given that test results continue to be extensively used when making important opinions about scholars, test inventors and experts have linked several strategies that can reduce, if not exclude, test bias and unfairness. Many representative exemplifications include

Seeking diversity in the test- development staffing and training test inventors and songwriters to be apprehensive of the eventuality of artistic, verbal, and socioeconomic bias.

Having test accouterments reviewed by experts trained in relating artistic bias and by representatives of culturally and linguistically different groups.

Ensuring that norming processes and sample sizes used to develop norm- substantiated tests are inclusive of different pupil groups and large enough to constitute a representative sample.

Barring particulars that produce the largest racial and cultural performance gaps and opting for particulars that produce the lowest gaps — a fashion known as “the golden rule.” (This particular strategy may be logistically delicate to achieve, still, given the number of racial, ethnical, and artistic groups that may be represented in any given testing population).

Webbing for and barring particulars, references, and terms more likely to be obnoxious to certain groups.

Rephrasing tests into a test taker’s native language or using practitioners to restate test particulars.

Including further “performance-grounded” particulars to limit the part language and word choice play in test performance.

Using multiple assessment measures to determine academic achievement and progress and avoiding using test scores to reject other information to make important opinions about scholars.


Despite Its character being a scientific and precise tool for dimension, cerebral testing is a culturally prejudiced procedure that results in differentiation against minority groups, particularly against minority scholars. Academic achievement and intelligence tests, the two types of tests most constantly used in public seminaries, assume that all people have the same behaviors tapped by the questions on the tests. They also presume that there is uniformity of academy classes in this country and that all who take the tests have the same installation with the English language. This cultural bias is compounded by other factors, similar to the item selection process, the content of the particulars, and the responses considered respectable to those particulars.

Corporate Social Responsibility Tips From Paypal

PayPal is one prominent example of a company that practices corporate social responsibility, and there are practical ways for even much smaller businesses to follow its lead.

More and more customers are saying that they are more likely to support businesses that align with their values.

If you’d like your business to support a cause, keep up with the news and read articles from various sources to find something that strikes a chord.

This article is for business owners interested in incorporating social responsibility into their business plans.

When it comes to corporate social responsibility, small businesses could learn a lot from PayPal. The credit card processing giant facilitates charitable giving in several ways, including its PayPal Giving Fund, which allows nonprofits to process donations without fees or deductions – and PayPal adds an extra 1% to Giving Fund donations made during the holiday season.

According to Sean Milliken, PayPal’s head of global social innovation, promoting social responsibility is part of the company’s broader business plan.

“People want to do business with companies that are aligned with a cause,” said Milliken in an interview with Business News Daily. “Giving back, contributing to society, [and] being a good corporate citizen is not only the right thing to do – it’s good for business.”

Even if your company doesn’t have the resources to embrace social responsibility on PayPal’s scale, there are good reasons to integrate some form of charitable giving into your business plan.


To learn more about PayPal’s offerings, read our guide to PayPal’s mobile card reader.

What is social responsibility?

In business terms, social responsibility is when companies take action to benefit society while increasing value for shareholders. To achieve social responsibility, corporations and the people who work for them must act in the best interest of society and the environment.

A business can achieve sustainability by holding itself accountable and being transparent about how it operates. Adopting these social responsibility principles in your business can help your employees and customers feel more fulfilled and positive toward your organization.

To become socially responsible, your business should enact policies that strive to benefit society. Some companies enact “green” policies focused on creating a more sustainable environment, while others establish moral responsibility and workplace ethics policies to ensure they act within their shareholders’ best interests.

Key Takeaway

Socially responsible businesses prioritize working for social good, weaving social responsibility into their business models.

2. Social responsibility helps align you with your customers.

While employee engagement is vital, your social responsibility efforts should also encourage customers to support charities your business supports. While charitable giving is built directly into some business plans, other companies find opportunities to give back that align with their business purposes, even if they aren’t necessarily written into the company’s founding principles.

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If you’re still developing your business plan, you can use our business plan template to craft one that incorporates social responsibility right from the beginning.

Milliken said there are various ways to connect customers to a cause. Customers are likely to rally around an immediate need, such as after a natural disaster. They are also likely to participate in giving that ties a social purpose to the product or service they’re buying. For example, TOMS Shoes has a “buy one, donate one” model.

Milliken said either approach could work for businesses that know their customers. “You can align yourself with a cause that is close to who you are as a business that will resonate with people and make natural sense. One way is not better than the other. [Social responsibility] does not have to be part of the business model from the start.”

3. Social responsibility can drive innovation.

While businesses giving back to their communities isn’t a new idea, integrating social corporate responsibility into a business’s very foundation is a relatively novel concept.

“Businesses have a long history of giving back, but I think the models for doing so have evolved,” said Milliken, who adds that the word “innovation” in his job title reflects PayPal’s commitment to changing the way the company thinks about social responsibility. “No longer will companies have a corporate social responsibility department where one or two people sit in an office writing checks to nonprofits.” [Read related article: Creativity Is Not Innovation (But You Need Both)]

Part of integrating giving into a company’s overall mission is responding to how technology is rapidly changing the way people donate money and support charities. Milliken said mobile technology and social media are drastically impacting how customers give and how businesses will reach those customers.

“We’re seeing a huge move toward mobile,” he said. “And that trend will continue to grow.

To the extent that we can embed ways for people to give anytime, anywhere, we have a real shot at increasing the level of charitable giving.”

Social media is also creating more opportunities for people to give. PayPal makes a point of providing the technology and tools to make this happen, according to Milliken. The key to leveraging social channels for charitable giving is to ensure the messaging is contextual and relevant to what the customer is doing at that moment, he said. “Social media can help us find people in these moments, giving us a better shot to get them to give.”

Ways your business can be socially responsible

There’s no one-size-fits-all approach to social responsibility initiatives, but here are some straightforward ways to get started.

1. Clearly state your company values.

Some entrepreneurs and small business owners are so focused on starting their business and launching their product or service that they don’t take the time to define their company’s values clearly.

Take a step back and reflect on what social good your company can support. Discuss this with your executive team and conduct employee surveys to learn what’s important to the company as a whole. Once you have a clear sense of your team’s values, you can look for projects and foundations to match.


If you feel stuck or uncertain about what cause you’d like to support, keep up with the news and read articles from various sources to note what sticks out to you.

2. Create realistic goals.

After establishing your values, think about how those values can inform your business goals. Brainstorm a list of actionable items you and your team want to achieve within a specific time frame.

Since it takes time to establish a process and routine, keep your early goals small and manageable. That way, you can achieve your goals more easily and won’t get discouraged. As you continue to connect responsibility policies and projects, you can expand and grow your goals.

3. Educate your employees and customers.

Once you create a plan, state your intentions to your employees. Let them know you value their insight when it comes to establishing your company values, and discuss the goals you’ve developed with their input. Clearly outline the social initiatives you’re focusing on and how you’ll make impactful change.

Include your social responsibility goals in your employee handbook and company policies. Some policies, such as paid time off (PTO) policies for volunteering, encourage employees to make a difference and demonstrate that your company looks beyond the financial bottom line.

Key Takeaway

When you establish your company as an ethical organization that cares about social issues, you’re more likely to retain top talent and attract high-level applicants in your hiring process.

After getting your employees up to speed, let your customers know about your new social responsibility goals. Your customers will feel that you’re engaging them on a human level and not just trying to sell to them.

Most customers like to know that the businesses they support align with their values. For this reason, launching a social responsibility initiative and sharing it with your customers is an excellent customer retention strategy and a way to interest new clients.

Sean Peek contributed to the writing and reporting in this article. Source interviews were conducted for a previous version of this article.

Interview: Bigboss Repo Maintainer Talks Security And User Responsibility

By jailbreaking their devices, most users usually know what they expose themselves to. When breaking the walls Apple has constructed to protect their security and privacy, jailbreakers put their fate in the hands of a handful of people. If done with basic principles in mind, jailbreaking can be very safe. I, for example, have been jailbreaking every iOS device I have owned since 2008, and I have yet to encounter any issue whatsoever.

Being cautious starts by being aware of what you install on your jailbroken device. Limiting yourself to the default repositories is good practice, as these repos do an outstanding job at analyzing jailbreak apps and tweaks before making them available for download, ensuring that the final user is as safe as possible.

But there is always that slight chance that a malicious tweak might have gone through the cracks and made its way into Cydia for millions of potential users to download. Nothing is 100% safe, but safety measures can be put in place to ensure the highest level of security. This is the job of repo maintainers.

We have talked to representatives of the two largest default repositories on Cydia to ask how they ensure the safety of their users. In a two-part series, we will publish their answers, starting today with Optimo, repo maintainer for BigBoss. Tomorrow, we will publish answers from Kyle Matthews of ModMyi.

BigBoss repo maintainer Optimo answers a few questions

How many tweaks are submitted each week or month on average? Out of these, how many are accepted?

I don’t keep data to refer to. Considering everything including tweaks, addons and apps, I would guess hundreds of submissions are processed, while dozens of those are new items each month. At our busiest times we can be rejecting nearly as much as we accept. Sorting through a lot of substandard items. Some rejected items are made acceptable and then reconsidered.

Can you please describe the process between the moment a tweak is submitted to BigBoss to be added to the repo and the moment this tweak is live on the repo and ready to be downloaded?

Most submissions of the free variety are processed within a day of submission and added to the repo each day. A new package is created based on the submitted content. The submitted item contents are examined and details organized so they fit into our packaging guidelines. Submitters tend to forget or leave out some details, so we will follow up by contacting them. The item’s depictions is tailored and then the new package information is synchronized to our servers. Paid items follow a similar process.

We regularly engage with the tweak makers. Many first-time submissions by new developers are missing details or contain a technical nuance that requires clarification. Part of the job I do is working with the submitters towards maintaining a standard of quality across our published items – to make sure we’re all on the same page. Hopefully our newcomer developers can receive guidance and learn something they can use to become better at their craft. A fun and engaging developer community is supported by shared values like respect for peers and their works, and from developers working to become responsible members of their community.

What are the safeguards in place during that process? A clear explanation of the security scans would help.

The significant process at work is a review of the submitted materials by an experienced repo maintainer. Care is taken in processing the variety of kinds of software that might be submitted. One downside of a semiautomatic scanner is that it can mean turning that responsibility over in part to the scanner. I prefer the more effective hands-on approach. We may employ a variety of developer tools to perform inspection on material if necessary.

There is careful oversight by the repo maintainer or processor, but users should keep in mind that nothing is perfect, not even the App Store review process. Our efforts are always working towards keeping a level of quality of the published software; that it works as described and does not do something odd or unexpected and respects users’ interests.

Contrary to what some may presume, the repos do not promise that our oversight provides 100% fool-proof protection against 3rd parties. We can make no such assurances. We aim to have packages that are reasonable to install. If something is not quite right, we will examine it more closely, contact the authors personally to open the discussion, and seek feedback from saurik and other knowledgeable developers.

If a submission looks odd or does something weird, it’s going to be held for questioning until we have satisfactory answers about it’s peculiarities. It is good to get to know the parties that are submitting when possible. As individuals, we all ought to take responsibility for our own security when using 3rd party software. In any case, the community repo maintainers do try to review items thoroughly, but we will not extend any guarantees about the security standing of the items we accept and publish.

Are select developers whitelisted by default and allowed for a faster approval process? Or are all developers on the same level when it comes to review process?

Items reaching us via our submission system are all treated the same and fairly. Free items naturally reach the public sooner than paid items, which require registration with Cydia Store before it can be published. I’m not sure how this question is related to the security subject, but new submissions are all roughly examined to the same degree.

How often do you catch malicious packages? Is there some sort of trend or has it been relatively stable over the years?

We are all lucky in some respects. Our community has not been frequently a target of wrong-doers, even after all these years. There are a few notable exceptions in the past years and very recently. Some times we catch submitted items that appear suspicious or have red flags, and wind up being held or rejected perpetually. Often those items do not make it onto the repo.

The repo maintainers have done their job well over the years, no doubt catching some things that are questionable and even objectively a danger to a user’s device, though that more often happens by innocent mistake by the developer than by malicious intent. This could trend further negatively, as platforms become more popular, that trend is more likely. Of course, any time is a good time to be sure you know what options are available to you for keeping yourself and your device secure.

The recent exceptions that have reached the public were supposedly targeted at the Chinese portion of the jailbreak base in order to repurpose the user’s personal/device details. That was caught by the public. Though it should not be taken lightly, the origins of that particular malware were not the default community repos but through other 3rd party sources. Packages that are malicous by intent are a rare thing to see submitted to the repos.

Back in July, a tweak called Lock Saver Free containing a trojan was added to the ModMyi repo. Have you ever had a similar situation happen to you? If so, how did that happen? How long did it take you to figure it out and take action? What preventive steps did you put in place to make sure this doesn’t happen again?

What steps are taken when a malicious tweak is detected?

Whether we find something in our review or we receive a report of a package with questionable contents or behavior, and depending on the nature of the complaint or symptoms, we will send out a notice to the author/submitter and remove the item from the repo as soon as possible. We will also notify saurik and Britta, and notify community members including the other default repos so they can be aware of the offending software or submitter so it is not mistakenly accepted elsewhere.

What are some of the worst malwares you’ve seen in tweaks submitted to BigBoss?

More often seen are honest mistakes by newcomer developers. They may not yet have read or learned about an important subject, or absorbed a moral principle that the community deems valuable, and it is reflected in their submission. With that in mind, packages that do things not in the best interest of the users, or that violate a packaging guideline, or ‘do the wrong thing’ to some degree, we will make efforts to sort that out before it’s accepted. That often means contacting the submitter to let them know about our objections and see if we can work with them to improve the offering before accepting it.

Check out this page for a list of known malware that have targeted both jailbroken and non-jailbroken iOS devices.

Your repo went down for a certain period of time last month. The next day, ModMyi repo went down too, which is something I can’t recall happening in the past. Was this just a terrible coincidence, or is there more to it than just bad luck?

On that day when both of the repos were unreachable, it most likely a denial-of-service incident upon our server(s). That is just a normal part of the business of operating a web server. That might happen on occasion but we’ve been relatively spared in that regard over the recent years. The day our repo was unavailable for some hours, I recall we had some unscheduled maintenance that unfortunately made for some real down time. That was just poor luck.

As iOS grows in popularity and is gaining ground in places like China, do you feel this makes the platform a bigger target for hackers? How do you feel about the general security of jailbreak tweaks developed for iOS going forward?

I don’t like the running narrative that many Chinese jailbreak users are more susceptible to hacks, but that seems to have just happened recently. Maybe it’s because of the software they used not being carefully checked before it was published. It might have lacked oversight. Perhaps the spread of iOS into China does mean the platform becomes a bigger target by volume.

Malware would not be specific to one nationality. The affected users might have been misled or they put trust into a software distribution system which failed at its oversight. Growth of iPhone in general means a larger jailbreak audience, which I suppose can mean more niches for hobbyists that like to dabble in bad software instead of good. Repo maintainers will need to adapt as the community grows. If you find something suspicious, please let the repo hosts know.

I feel that users need to take some personal responsibility to assure their own device security. Repos fill a role and do their best to make our community safe and organized, but it’s not a perfect system. If one blindly accepts that all the items published by the repo are free from risks, that may be letting your guard down. 3rd party software can carry risks, although historically the incidents are very low amongst the default community repos. By being diligent about the subject and researching the topics of device security, risks can be minimized in your day to day uses. You can start learning more here.

Anything else you’d like to say or clarify?

As maintainer for the largest tweak hosting repo, I keep closely in contact with our developer community on purpose to have a strong working relationship. I try to meet our developers where they like to hang out: Twitter, reddit, IRC. This cooperation is often established even well ahead of their submissions to the repo. This relationship is invaluable in my opinion. Often we find that we’re helping guide them down the right path, and make healthy decisions about the liberties they have as a 3rd party piece of software. Education about these subjects has benefitted all parties involved, from developer to users. The repos will work quickly to rectify a bad situation that is uncovered.

There’s been a public concern recently over reports of ‘malware’. The subject of malware is broad and sometimes intimidating. And some of the subject is rather new to mobile platforms. Our community has been relatively free of the concern of this subject until recently. What the future holds in this regard is unknown, but the default repos will continue to work for the community. Knowledge is power, and educating one’s self can make you safer. Quality information can help dispel myths and fears — upon hearing something vague about reports of malware affecting jailbreakers, for example. We need not jump to conclusions on this subject, but sharing with your peers something odd you experience, and posting the subject on forums can help us all learn quickly and adapt.

If your device is jailbreakable it also might not be secure without some additional configuration. The nature of jailbreaking means that your device is potentially less secure once the software ‘hole’ is exploited and known to the public at large. Over time security researchers find more holes in iOS security, and Apple makes updates to to fix the security. Their iOS updates often mean losing your jailbreak, however. So keeping your jailbreak may be a compromise of security. If you are generally concerned about security and wish to keep your jailbreak, you may want to research and get to know the options available to you for making your device and data as secure as you need it to be. It can be helpful to secure your personal device from software threats and from physical access. You can read more here.

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