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The Ethics of AI - Avoiding Bias in AI Decisions

AI has made a big splash in the news for its role in murder-by-numbers software, self-driving cars and other life-changing technologies. But it also raises ethical questions about its potential for bias in the way that it makes decisions or interacts with people. AI ethics is a field that works to ensure that future machine behavior will be ethically acceptable, much like the moral philosophy used to guide human choices.

For example, an algorithm might disproportionately hurt certain groups of people by giving them lower credit scores or assigning higher risk ratings in law enforcement. These unintended consequences can have a profound impact on individuals, families and communities. As the technology becomes increasingly widespread, companies must incorporate AI ethics into their design processes to avoid creating biased algorithms.

The concept of AI ethics echoes long-standing philosophical debates about how to categorize ethical principles and behaviors (Rowlands, 2006). While AI is an engineering field, it’s also rekindling century-old discussions about decision-making, human actions and rationality. As a result, there are many questions that can’t be answered by applying one of the prevailing ethical theories to a new situation. This reflects the fact that the issue is more about what machines are doing than how they’re doing it.

Nevertheless, the debate about the nature of AI’s autonomy is an important one. The discussion about the meaning of ‘behave ethically’ is a key What is techogle? part of this debate. In this context, virtue ethics can help to ensure that the purpose of AI is to seek humans’ flourishing, which is the highest good. This helps to prevent moral schizophrenia, where machines are influenced by beliefs that do not align with our own (Taddeo and Floridi, 2018).

As the technology develops, a new field of AI ethics has emerged. This involves the study of how to certify that AI’s behavior will be ethically sound. This is a complex task, especially as machines become more autonomous. While there are no clear-cut answers, a number of tools can be used to identify and correct biases in algorithms. These include training datasets that are representative of the population to eliminate the effect of innate bias and incorporating bias detection into the code to prevent it from creeping in.

It’s clear that this is not an easy task, but the goal of ethical AI should be a guiding principle for designers, rather than something to be avoided. For instance, when technology news a lending bank’s AI system gives loans to people of different races, the management team and AI teams should decide whether they want the model to aim for equal consideration (equal numbers of loans are processed for each race), proportional results (the success rate of each race is relatively equal) or equal impact (the amount of money given to each race is equally significant). Incorporating AI ethics into the design process can help with these decisions and ensure that the system’s outputs reflect society’s values.