Artificial Intelligence can help humans make fairer decisions, but at the same time, it can embed human and societal biases and deploy them at scale. It is often not the algorithm but the underlying data that is the main source of the issue.
Bias in AI is one of the hottest topics in the industry right now. AI would seem like the perfect solution to mitigating human bias. Unfortunately, all models are made by humans and reflect human biases. AI, at its core, is data and can only be as flawed as the human processes it simulates. It can reflect the biases of the designers, organizational teams, data scientists, and data engineers. They also reflect the bias ingrained in the data itself.
Bias creeps into even the most highly scrutinized algorithms long before the data is collected and also at any of the stages involved in the deep-learning process. When the AI models are created, utilized, and maintained properly, they can help humans make unbiased decisions. But this means creating a reliable and repeatable process for this purpose.
Eliminating Bias in AI Requires Human Collaboration
To identify and minimize the effect of human biases in AI, it is crucial to run algorithms alongside human decision-makers, analyzing results and explanations for the possible differences.
Most algorithms are trained on large datasets to create greater accuracy and reduce bias simply by their size. The wider the field, the less likely it is to contain pronounced biases, whereas a small dataset could be extremely biased.
The process should not be left to AI developers or IT departments. To reduce as much bias as possible and to employ AI as a tool to find and eradicate bias, executives and business leaders need to put their heads together to develop best practices and ethical standards.
Organizations need to frequently examine their algorithms for biases and delete any biased associations they discover. This requires an organizational knowledge of how, where, and when AI algorithms are deployed.
Business leaders should also support progress by making more data available to practitioners and researchers across the organization, working on these issues while being sensitive to potential risks and privacy concerns.
In addition to internally gauging the performance of the algorithms, it’s crucial to seek out feedback from the customers as well. This can help identify content that was marketed to them inappropriately, including irrelevant emails, recommendations from conversational AI assistants, and other algorithmic errors.
Frequent Testing and Auditing
To protect themselves against biases in algorithmic decision-making, organizations must conduct periodic audits that ensure algorithmic hygiene before, during, and after implementing AI tools.
Organizations should also look at employing the right tools and platforms to provide transparency and relevant metrics. There should also focus on improving data collection through more conscious sampling and also use third parties to audit data and models.
If the humans tuning and auditing AI models are diverse in gender, geography, race, ideology, and more, the solutions they work on are more likely to end up unbiased. They are more likely to recognize a wide variety of biases when encountering them in solutions and datasets.
There will be bias as long as humans are involved in making machines. But some tools and processes can help mitigate AI bias, and the industry is beginning to recognize how crucial it is to use them.
If AI is properly tuned and trained by people and targeted algorithms, it can provide solutions to the bias problems that we encounter in our human interactions. The best way to reduce bias is not to minimize the involvement of humans in the creation, deployment, and maintenance of AI; it may be to put more humans in the loop than ever before, and preferably, a more diverse set!