Artificial Intelligence has come a long way in the last few years. For a wide range of tasks, AI-based software is achieving or surpassing human-level results. We’ve got image classification, speech recognition, driverless vehicles etc. from the field of AI. There is no doubt that in the coming years, artificial intelligence will become an integral part of our daily lives. But in the end we human controls AI systems and human decision-making may be faulty. Here comes the term AI bias.
As artificial intelligence (AI) makes inroads into sensitive areas like healthcare, criminal justice, and recruitment, algorithmic bias can have negative implications for human well-being, as well as a negative effect on society. The algorithmic bias is a reflection of the flaws in AI systems. This is the lack of fairness that results from a computer system’s success.
In this essay, I’ll try to cover the present findings that are available in the AI bias field. So let’s begin.
What is bias in Artificial Intelligence?
The intrinsic prejudice in data used to construct AI algorithms is known as AI bias which can lead to discrimination and other social consequences. In other words, an anomaly in the performance of machine learning algorithms is AI bias. This may be due to biases in the training data or prejudiced assumptions made during the algorithm creation process.
AI systems contain biases due to two reasons. They are cognitive bias and lack incomplete data. Cognitive bias is effective feelings about an individual or a group based on a person’s or a group’s perceived membership in that group. If data is missing, it may not be representative, and thus may contain bias. Most psychology scientific studies, for example, contain findings from undergraduate students, who are a distinct category that may not represent the entire population.
Examples of bias in Artificial Intelligence
Amazon is one of the world’s most powerful technology companies. As a result, it’s no wonder that machine learning and artificial intelligence are heavily used by them. Amazon discovered in 2015 that its algorithm for recruiting workers was biased against women. The explanation for this was that the algorithm was trained to prioritize women over men. Based on the number of resumes submitted over the previous ten years it favours women, as in the last ten years the majority of the candidates were men.
Researchers discovered in October 2019 that an algorithm used on over 200 million people in US hospitals to predict which patients will possibly need additional medical treatment favoured white patients over black patients. For a variety of factors, black patients with the same conditions had lower healthcare costs on average than white patients with the same conditions.
We see human bias in tech social networks daily. These prejudices result in biased machine learning models because data from tech platforms is later used to train ML models. Facebook’s advertisers were able to target ads based on gender, race, and ideology in 2019. For example, women were given priority in job advertisements for nursing and secretarial positions, while janitors and taxi drivers were predominantly marketed to men, especially men from minority backgrounds.
How bias occurs in Artificial Intelligence
Today’s bias in algorithm and machine learning systems arises from a variety of factors. Deep learning models operate by using the pattern recognition abilities of neural networks. As a result, deep learning models cannot be specifically biased by architecture, and any appearance or cause of bias is external to the neural network’s architecture and design. Machine learning models and AI systems generate outputs that are simply a representation of the training datasets. As a result, we must take a step back and examine the training data.
Bias can appear in training data in two ways. One is the data you collect is unrepresentative of fact, or it represents current biases. For example, if a deep-learning model is given more images of light-skinned faces than dark-skinned faces, the first case could occur. Darker-skinned people will inevitably be missed by the resulting facial recognition device. The second instance arose when Amazon found that its internal recruitment method was refusing female applicants. It learned to favour men over women because it was trained on previous hiring decisions that favoured men.
Bias may be introduced during the data preparation stage, which includes deciding which attributes the algorithm should take into account. An “attribute” inability to repay modelling may be the customer’s age, salary, or the amount of paid-off loans. An “attribute” in Amazon’s recruitment tool may be the candidate’s gender, level of education, or years of work experience. Choosing which attributes to consider or disregard can have a huge impact on the model’s prediction accuracy. However, although its effect on accuracy is simple to quantify, its effect on model bias is not.
How to fix the bias in Artificial Intelligence?
It’s difficult to build non-biased algorithms. To build non-biased algorithms, the data used must be bias-free, and the engineers developing these algorithms must ensure that they are not transmitting any of their prejudices.
The data that is used must reflect “what should be” rather than “what is.” This means that it’s common for randomly sampled data to contain biases because we used to live in a skewed environment where equal opportunity was only a pipe dream. That being said, we must take reasonable action to ensure that the data we use correctly represents everybody and does not discriminate against any group of people. We can say, if there had been an equivalent amount of data for men and women in Amazon’s recruiting algorithm, the algorithm would not have discriminated as much.
We must control our modelling processes to ensure that we are ethical in our activities, as both individuals and businesses have a form of social accountability. This can take a variety of forms, such as hiring an internal enforcement team to implement some sort of verification for any algorithm produced.
We should aim to ensure that indicators like true precision and false positive rate are consistent when comparing different social classes, whether gender, race, or age, based on the examples above.
While there are no fast solutions for eliminating all biases, there are high-level guidelines from consultants such as Mckinsey that highlight the best practices for AI bias minimization:
Why removing bias from AI is difficult?
Since you do not know the long term impacts of your data and decisions until far later, the introduction of bias isn’t always apparent during the model’s creation. It’s difficult to identify where the prejudice came from and then find out how to get rid of it after you’ve done so. When Amazon engineers realized their tool was discriminating against female candidates, they modified it to ignore specifically gendered terms like “women’s.”
Many of the mainstream deep learning practices aren’t designed with bias identification insight. Deep-learning models are checked for reliability before being implemented, which seems to be an ideal scenario for catching bias. In practice, however, testing typically takes the form of software engineers randomly dividing their data before training into two groups: one for training and the other for testing after training. That is to say, the data we use to measure our model’s output has the same prejudices as the data you used to train it. As a consequence, it may fail to detect distorted or biased outcomes.
Computer scientists are often trained to frame problems in ways that are incompatible with the right way to think about social issues. Andrew Selbst, a postdoc at the Data & Society Research Institute, identifies the “portability pit” in a new article. Designing a device that can be used for multiple tasks in multiple contexts is considered best practice in computer science.
It’s still unclear what a bias-free workplace should look like. This isn’t just true in computer science; philosophers, social scientists, and lawyers have all debated this topic in the past. What makes computer science unique is that the principle of justice must be established mathematically, such as balancing a prediction system’s false positive and false negative rates. However, as researchers have found, there are many mathematical concepts of justice, all of which are mutually exclusive.
There have been several techniques for applying fairness constraints to AI models. The first involves pre-processing the data to ensure as much consistency as possible while minimizing any relationship between outcomes and protected characteristics, or creating data representations that do not include sensitive attribute information.
Post-processing methods are the second approach. To meet a fairness restriction, these alter some of the model’s predictions after they are made. The third strategy either uses an opponent to limit the system’s ability to predict the sensitive attribute or imposes fairness restrictions on the optimization mechanism itself.
Other enhancements are also being developed and tested by researchers. On the data side, researchers have improved efficiency for covered groups by introducing more data points to text classification tasks. Creative training methods, such as transfer learning or decoupled classifiers for various classes, are effective in minimizing differences in facial recognition technologies.
How do you prevent AI bias?
Experts have seen the lack of diversity in the technology industry firsthand. While the lack of diversity within organizations might not be deliberate, it should be addressed urgently. We might see the inclusion of parts of datasets that were previously ignored if we have more people with diverse backgrounds collecting data and developing AI systems.
Although technological and multidisciplinary research has made considerable progress in recent years, further investment in these efforts will be needed. Business leaders may also aid development by making more data accessible to researchers and professionals working on these topics through organizations while being mindful of privacy concerns and potential threats.
Models are seldom stable throughout their lives. Deploying the model without a way for end-users to provide input on how the model is performing in the real world is a common, but costly, mistake. Opening a discussion and feedback platform will help to ensure that the model continues to work at its best for everyone.
You’ll want to keep reviewing the model, not only based on customer reviews, but also by having impartial people check it for shifts, edge cases, biases you may have overlooked, and so on. To improve your model’s consistency, make sure you get input from it and give it your own, iterating toward higher accuracy.
Engineers working on AI systems, in the vast majority of cases, do not have an intrinsic bias or discrimination towards a particular category of people. Despite this, there may be a disconnect between the current world in which the existing technologies are supposed to operate and how the designers plan for them to be used due to a lack of exposure to other cultures and walks of life. One approach to reducing algorithm bias is ethics education within businesses and organizations. Employees who are educated on cultural and lifestyle differences become more mindful of communities in society that may have been ignored or not even considered.
Additional data, analytics, and artificial intelligence (AI) may be a powerful new tool for investigating human biases. This could include running algorithms alongside human decision-makers, analyzing outcomes, and looking into potential reasons for discrepancies.
When a company discovers that an algorithm trained on its human decisions is biased, it should understand how the underlying human behaviours ought to improve. Organizations will be able to benefit from recent developments in assessing justice by applying the most applicable measures for bias to human decisions as well.
Interdisciplinary participation, including ethicists, social scientists, and specialists who better understand the complexities of each application field in the process, would be needed to make further progress. As the field advances and practical expertise in real-world applications expands, a crucial part of the multidisciplinary approach will be to constantly consider and assess the task of AI decision making.
There is a lot of work to be done to remove AI bias. But many researchers are doing their best to develop a new way to reduce bias. They’ve used several methods, including algorithms that help identify and reduce hidden prejudices in training data or mitigate the biases learned by the model regardless of data quality.
The issue of bias and prejudice in AI-based decision-making processes has received a lot of attention recently from research, business, culture, and lawmakers, and there is an ongoing discussion about AI’s benefits and threats to our lives and civilization. Biases are deeply ingrained in our societies, and it is a fallacy to conclude that the AI and bias issues can be solved solely by technological means. Nonetheless, since technology represents and projects our biases into the future, technology makers must consider the limitations of the technology and suggest precautions to avoid potential pitfalls. Equally important is for technology developers to recognize that technological innovations without a social or legal foundation will fail, necessitating multidisciplinary approaches.