Bias is one of the hottest topics in AI right now, and the world at large is grappling with issues like racism and inequality. Every day, people face situations in which prejudices based on race, gender, disability, religion, and many other differences adversely affect ethical behavior and fairness. Even the most well-intentioned and educated people find it difficult to overcome lifelong prejudices against diverse cultural factors, longstanding conventions of unequal treatment and unfair preferences. Even if conscious biases can be suppressed to some extent, unconscious biases that we are not aware of can be quite troublesome.
AI seems to be the best bet for solving the human bias problem. To eliminate bias from decision making, why not leave it to AI alone? AI, which is artificially created intelligence, does not have biases. Can't the use of AI make day-to-day decision-making processes like hiring, approval, and diagnosis completely fair?
Unfortunately, AI suffers from the same biases that humans do. In fact, credible research suggests that the problem of bias in AI is only getting bigger and will become even more pronounced in the future. As we move towards a more equitable society, it is clear that only unbiased AI systems will be used in the long term. How do AI solutions become biased in the first place? Also, what can I do to get rid of this problem?
What biases AI
Humans are biased creatures regardless of their intentions. AI solutions are created by humans, so the human involvement in AI is what creates the bias. Bias in AI does not mean that it creates its own racial or gender preferences. Bias arises when the people involved in developing the solution use biased data, perhaps unknowingly learning certain biases in themselves or others.
Most algorithms train on large datasets simply to increase the amount of data to increase accuracy and reduce bias. While wider fields are less likely to introduce significant bias, small data sets can lead to extreme bias. However, many AI algorithms are only able to use constant data and remain trained on data that contains racism, ideology, and other biases. When these solutions are used as decision-making aids in public sectors such as government, health care, and education, or when making employment decisions, such biases become visible and people use them. You may stop trusting AI. If artificial intelligence is far less fair than humans, why should we use it?
How to reduce bias in AI
Properly developed, used and maintained, AI can help us make unbiased decisions by removing the unconscious bias that plagues us. But that requires creating a repeatable and reliable process. Please refer to the following steps:
Crowdsource multiple models
The reason CrowdANALYTIXrelies on crowdsourcing is that multiple models can be compared with each other even if there are individual biases in the model building stage. That way, you can choose the model (either a single model or a combination of multiple models) that has the lowest bias when testing.
Audit AI
If humans created AI, then humans are responsible for bias in AI solutions. Humans should be able to analyze solutions, find and eliminate biases in them. A reliable monitoring and regulation system is important. Human audits and bias detection algorithms can further reduce AI bias.
deal with specific, targeted problems
If you ask AI to solve a problem that is too broad or vague, it can be difficult to spot biases in the vast amount of data it uses. The more detailed the coverage, the easier it is to reduce the bias.
Have a good understanding of your data
If you carefully analyze the data you train on for biases, you won't get any unexpected results. If the data set is inadequate, it is likely that efforts to collect even more appropriate data will need to be expended to resolve it.
AI that allocates diverse human resources
If the model is audited and tuned by a diverse group of people (race, gender, ideology, etc.), the resulting solution will be less biased, and various biases in the solution and dataset will be more recognizable. Become.
Aiming to develop explainable AI
If you can easily explain your solution to outsiders, it will be easier to consider and identify what is biasing the AI. Additionally, if your solution is explainable, you can ask a wider variety of people for feedback.
AI tuned with well-learned, unbiased, human-targeted algorithms could offer a solution to the problem of bias in human interactions. However, keeping biased humans out of the creation, development and maintenance of AI requires a lot of back-and-forth between humans and machine learning. Perhaps the best way to reduce bias is to involve more people than ever before.
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Source:https://www.crowdanalytix.com/can-ai-mitigate-human-bias/
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