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Why is it important to fight against biases in artificial intelligence?


By Sanjeev Menon, Co-Founder and Head of Product, E42

Historically, humans had various biases and prejudices like racism, classism, anti-Semitism, ableism, sexism, misogyny, etc. Whatever human society you live in would have been strewn with prejudices based on sex, gender, religion, complexion, beauty, social class, etc. in the past. However, now we know better. The extent to which human bias can infiltrate artificial intelligence (AI) systems and cause detrimental damage is a hot topic in the tech community. To put it simply, AI bias is a problem that arises when an AI algorithm generates results that are systematically skewed due to false assumptions made during the AI ​​training process. The rapid adoption of AI across industries makes it imperative to fix and eliminate the problem for the technology to have a positive impact on the world at large.

Moreover, when we create an intelligent entity like an AI agent, we have to shape it according to the ideal world we want to live in rather than the one with prejudices and prejudices that we had. To dig deeper, let’s understand the causes of AI biases, their potential impact, means of prevention, and the reliability of AI.

What causes AI bias?

The main reasons biases creep into AI algorithms are:

Cognitive bias – unintentional cognitive errors generally involving decision-making, which distort judgments and choices. Based on established concepts that may or may not be accurate, this type of bias results from the human brain’s attempt to streamline the processing of environmental data. Psychologists have identified and described over 180 types of cognitive biases, including confirmation bias, hindsight bias, selfish bias, anchoring bias, availability bias, framing effect, and attention blindness.

A lack of comprehensive data – data that is incomplete and not fully representative of all stakeholders will be biased. AI can be trained on datasets that underrepresent a particular group. Models trained on data exhibiting social and historical injustice. For example, a candidate selection AI model trained on historical data with gender as a characteristic will favor male candidates.

Selection bias – the data could be unrepresentative or selected without adequate randomisation. Oversampling a particular demographic group could result in an AI model biased for or against that demographic group.

Bias feedback loop for user-selected data – people who organize and describe images of adventure sports may associate men with the sport, and the comments reinforce the model’s bias.

Unintentional bias – The AI ​​system can potentially detect inappropriate statistical connections – a creditworthiness AI can consider age as a parameter and deny loans to seniors.

How is AI bias harming society as a whole?

Although AI is supposed to free us from human limitations, the flip side is that it also depends on humans to learn, adapt and function properly. AI systems are designed to analyze vast amounts of data as they perform their tasks. They are able to detect patterns and trends in the data pool and eventually use it as information to perform actions or help humans make better decisions.

Sometimes the training data used in AI models is not substantial or diverse enough, resulting in misrepresentation of certain demographic groups. This is quite dangerous and researchers around the world are concerned that it is possible that machine learning AI models are inspired by human biases and end up exhibiting discriminatory behavior based on gender, race, ethnicity. ethnicity or orientation.

Besides insufficiency, training data can also be rendered inaccurate due to human biases leading to over-representation and/or under-representation of certain types of data, instead of assigning equal weight to different data points. This is a classic example of how biased results can seep into the public domain and lead to unwarranted consequences such as legal ramifications or lost financial opportunities.

Although biases in AI systems are sometimes perceived simply as a technical problem, they pose a serious threat to humans on a larger scale. A combination of human, system, and computational biases can lead to dangerous outcomes, especially in the absence of explicit guidelines to manage the risks of deploying AI systems.

Why is this such a big problem? The training data that AI systems use to make choices may contain biased human judgments reflecting historical and social injustices. By fostering mistrust and providing biased results, it reduces the overall potential for the use of AI in business and society at large.

Prevent AI bias
Collaboration between social scientists, policymakers, and members of the tech industry is absolutely necessary to address the problem of bias in artificial intelligence. Today, companies can make real efforts to ensure that the algorithms they create promote diversity and inclusion.

● Examine history – businesses can practice fairness by being aware of where AI has failed in the past and leveraging industry experience to fill in the gaps.
● Keeping inclusiveness in mind – large companies can ensure that the models they build do not inherit biases present in human judgment – ​​it makes tremendous sense for them to consult humanists and specialists social sciences before embarking on the design of AI algorithms.
● Targeted testing – the performance of AI models can be scrutinized across various subgroups – testing them systematically to find issues that aggravate some metrics may be invisible or hidden.

The complexity of the social circumstances in which AI systems are used as well as the potential problems with certain data collection methods cannot be solved by definitions and statistical measures of fairness alone. It is extremely crucial to consider when and how human judgment is required.

Companies should work on ethical AI and establish frameworks and controls to prevent AI bias
They need to define what AI ethics means to them in terms of people first, care about the societal thought process across the organization, and build cross-functional teams to govern training and the use of AI. Additionally, they should adopt a lifecycle approach to AI bias in which various bias assessments are performed, from initial concept through development to the post-release lifecycle.

How responsible can AI be?

The simple answer is, only as much as humans build and deploy AI what it needs to be!

AI provides many benefits for industries and the economy, and solves the most serious social problems, but only when humans collaborate and strive to address AI bias responsibly. When AI models trained on human decisions or behaviors exhibit bias, organizations need to think about how human-driven processes could be improved by responsibly building and deploying AI.

For example, deciding when exactly an AI system has reduced bias enough to be released for widespread use – such decisions cannot be supported by an optimization algorithm, and correct answers cannot be determined by a machine. Instead, human judgment and processes should be used to set standards, drawing on social science, law and ethics to ensure that AI is used fairly and without bias.



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