Should Artificial Intelligence (AI) Take Over the Decision-Making Process?
Artificial intelligence (AI) is increasingly being used in sensitive areas such as healthcare, the criminal justice system, and hiring processes. Courts today use AI systems to assess offenders’ risk of recidivism and the likelihood of flight for offenders awaiting trial. One such algorithm is the Arnold Foundation algorithm, which uses 1.5 million criminal cases to predict defendant’s behaviors during pretrial hearings (Zavrsnik, 2020). With such advancements, there is a growing debate on whether AI should fully replace human decision-making due to its ability to eliminate human bias. Sources have shown that contrary to popular belief, AI decisions are not always less biased than human systems. For instance, a 2016 investigation by ProPublica showed that data–driven AI used by courts to assess the risk of recidivism was biased against minorities and people of color (Silberg & Manyika, 2019). In the UK, a computer program that was used to determine which applicants would be invited for interviews into medical school was found to be biased against female applicants and those with non-European names (Silberg & Manyika, 2019). This text analyzes the potential causes of such biases in the use of AI and identifies what decision-making is likely to look like in the future.

Potential Causes of Bias in AI Use

Sources have identified several causes of algorithmic bias in AI systems. Algorithmic bias could be introduced into the data through pre-existing cultural, social, and institutional expectations that perpetuate historical or societal inequities (Silberg & Manyika, 2019). For instance, data may be fed using words that exhibit gender stereotypes that characterize society. A hiring algorithm designed to favor words such as ‘captured’ or ‘executed’, is, for instance, more likely to be biased against female applicants as such words as more common among men’s applications than those of female applicants (Silberg & Manyika, 2019). One algorithm that exhibited this kind of bias was that developed by Amazon engineers to aid in the company’s recruitment (Lee, Resnick, & Barton, 2019). The algorithm was designed to recognize word patterns in applicants’ resumes rather than relevant skills. The AI software penalized any resume that contained the word ‘women’ in the text and downgraded applicants who attended women colleges, giving rise to gender bias (Lee, Resnick, & Barton, 2019). If historical biases are factored into the data, the resultant model will make the same kinds of wrong judgments that...…Manyika, 2017).

As a best practice, sources recommend that proper audits and impact assessments are conducted to check for fairness and the risk of bias before AI systems are deployed (Silberg & Manyika, 2017). Further, there may be a need for experts to collaborate so as to review AI systems on an ongoing basis and suggest frameworks that may help to improve fairness (Silberg & Manyika, 2017).

There is also a need to raise the standard for human decision-making to minimize the risk of human bias. Silberg and Manyika (2017) suggest rethinking the standards used to evaluate whether human decisions are fair and when they increase the risk of bias. One way to increase the credibility of human decision-making is by using AI to examine human biases by running algorithms alongside human decision-makers, comparing results, and identifying potential explanations for differences (Silberg & Manyika, 2017). In this regard, if an organization discovers that an algorithm founded on human decisions exhibits bias, it should not just discontinue the use of the algorithm, but work on changing the underlying human behaviors as well (Silberg & Manyika, 2017). This will ensure that human systems are held accountable in the same way as AI systems.…