With AI systems being used in practical environments, there is a concern about how they operate ethically and do not participate in biased ways. AI ethics is a relatively new field of study that is interested in defining proper approaches and tools to deal with these problems.
AI’s first major concern is bias in the service to be offered and the system to be developed. Like any technology that evolved from human society, AI can contain preexisting societal biases inherent within the developers themselves if they are not actively removed. For instance, a hiring algorithm trained using historical employment data that has gender bias will tend to rate male candidates higher than females, thus promoting discrimination. Such biases must be addressed to gain acceptance of AI-powered systems and avoid unfair consequences.
There are several techniques currently being researched and deployed to detect and reduce biases in AI models: There are several techniques currently being researched and deployed to detect and reduce biases in AI models:
- It is even better to ensure that the training data is diverse and has minimal possibility of bias because it is better to prevent models from even learning the wrong correlations in the first place. In practice, achieving a scenario where data is perfectly unbiased is practically impossible.
When the model is being developed, testing methods such as cross-validation are used, whereby different subgroups are tested. Before the model is deployed, one may discover that one subgroup is being predicted poorly. Those with biases can then be adjusted and eliminated from the models.
Another similar technique is adversarial debiasing, in which extra models are employed to automatically address a number of biases within the algorithm without compromising accuracy.
Calibrating the model’s thresholds can assist in rectifying skewed score distributions that may be derived from post-processing model results.
Transparency reports provided from monitoring the model decisions after deployment help identify any bias that may have occurred in the model, and this can be done almost immediately.
However, technology solutions may not be enough; governance and policy advances regarding how AI and its results are made comprehensible, auditable, and overseer by the public will likely remain crucial to keeping the field ethical. Such systems are exemplified by the AI Now Institute and other multi-stakeholder initiatives seeking to design appropriate regulatory structures concerning AI’s effects on society.
Therefore, it will call for continuous participatory efforts by AI developers, policymakers, and affected populations. It is important to note that the positive impact AI can have on society can only be unleashed through concerted efforts.
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