Algorithms Need Management Training, Too

These days, advances in technology are making it possible for artificially intelligent algorithms and automated systems to undertake a wider range of decisions, from granting loan packages to deciding which candidate to hire for a particular job. Although automation technology is undeniably a useful tool in reducing human labor, as well as speeding up decision-making and removing costly human biases, it also creates risks and potential harms. In short, there’s an ethical responsibility to ensure these systems are designed and used fairly and respectfully.

The tech and business world are starting to recognize the need to build safeguards into automated decision-making systems, in order to prevent data-driven racism and discrimination, as well as to create legal compliance. Governments too are catching on, with nations such as the United Kingdom enacting legislation to protect citizens from the potential harms of algorithms.

It’s therefore important for businesses and organizations to take a proactive approach in curbing potential misuse. Here are three practical steps for addressing the ethical and legal implications of automated systems:

1. Analyze how decisions are being made. Before adopting an automated decision-making system, it’s essential that organizations gain an understanding of how decisions are being made and whether decisions are being made with potential biases.

2. Involve a third party. Inviting a third party with expertise in the relevant field is a great way to ensure your decision-making system is free of subtle biases and compliant with the law.

3. Build a feedback loop. Automated decision-making technology needs to be consistently monitored and checked to ensure it remains fair and unbiased, and a feedback loop enables organizations to quickly identify and address any potential problems.

It’s essential that businesses and organizations take steps to ensure automated systems in the workplace make fair and ethical decisions. By implementing a few simple steps outlined above, organizations can make sure their automated decision-making systems are curbing potential harms and abuses.






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