Understanding how to mitigate biases in artificial intelligence is going to be increasingly important as this technology is used to make an increasing number of decisions — not just within the enterprise, but throughout the entire human ecosystem. Business and organizational leaders must make sure that AI systems they employ are improving on the decisions made by humans, and it is their duty to promote advances in research and standards that reduce biases in AI.
As AI uncovers more about human decision-making, leaders may want to think about whether agents used in the past were sufficient, and how AI might be useful by uncovering long-standing biases that might not have been noticed. Through training, process design, and culture changes, companies can enhance actual processes to mitigate biases. Decide use cases in which automated decision-making should be preferred, and in which cases humans should participate. R&D is critical for minimising biases in data sets and algorithms. Because AI is already used for life-altering decisions, minimizing bias and maximizing fairness is critical. Some promising systems are using a mix of machines and humans to mitigate bias
While supervised models increase the scrutiny on biases when selecting data, this supervision may introduce human biases to the process. In many cases, AI may decrease subjective interpretation by humans in data, as machine learning algorithms learn to only take into account variables that increase the accuracy of predictions, according to the training data used.
Given human data that the algorithms are exposed to biases, if we view results in rational terms, we can take advantage of the capabilities of machine learning to identify anomalies. Companies can detect human biases, and they can use that knowledge to figure out what causes biases, modeling AI. With that in mind, we can identify several different cognitive biases humans have unintentionally programmed into AI systems, which could put meaningful limits on the way that smart machines work.
While it is easy to notice that humans tend to rely on heuristics when making decisions (or so we thought), these biases are automatic and unconscious, and may be difficult to detect. AI systems learn to make decisions from training data, which may incorporate distorted decisions made by humans, or reflect historical or societal inequalities, even when sensitive variables like gender, race, or sexual orientation are removed. AI algorithms and data sets can be examined for biases in order to improve outcomes.
AI can help detect and mitigate the effects of human bias, but can also worsen the problem by embedding and spreading bias on a large scale across sensitive application domains. In conclusion, using AI can help address deep-seated biases, so organizations can break down critical barriers to improving decision making, but it should never be considered as the silver bullet business solution.
Yet, even the human decisions made in delicate areas may have shortcomings, formed by personal and social biases, which are often subconscious. Implicit biases are manifested even in machine decision making, creating lasting effects on human dignity and opportunities. To address bias, one should first ensure that the data used to train an algorithm is not itself biased, or, more precisely, that an algorithm is capable of recognising biases in this data and alerting humans to them.