India should be prepared for bias in artificial intelligence by developing AI algorithms with real-world, structured, and diverse data.

Artificial Intelligence has become a boon for humanity. It is purposeful not only in day-to-day lives but also in business, healthcare, and other sectors as well. Although there are multiple benefits of using Artificial Intelligence, there are some valid concerns regarding AI technology. Concerns like threats to humanity, surpassing human intelligence, threats to our jobs, or whether AI judgments are trustworthy are rising. Such AI biases and the concerns regarding AI algorithms are our focus areas of this article.

Then comes the question, what is AI bias?

"AI bias is an anomaly in the output of machine learning algorithms, due to the prejudiced assumptions made during the algorithm development process or prejudices in the training data."

There can be two major pathways of AI bias:

• Cognitive biases: The unconscious errors in human thinking which, affects one's judgments and decisions. Such biases could seep into the algorithms through a training data set which contains those biases and/or by designers who unknowingly introduced the biases to the model.
• Incomplete data: Another pathway includes providing incomplete data to the algorithm. For example, a set of data may be devoid of a proper representation of the lgbtq+ community, leading to biases.

AI is prevalent in every sector and its applications and therefore it is very crucial to eliminate bias from the algorithms.  Such biases not only affect the decision-making process and outcomes but also magnify and propagate them. These could also lead to the replication of societal bias like gender inequality. The under-representation can also lead to an increase in the social-economic gap. The data containing biases are used to make decisions and will lead to biased results, affecting society as a whole. By eliminating bias, AI algorithms will be completely free from bias, resulting in unbiased data-driven decisions.

Ways to Eliminate Bias:

• Structured, real-time data should be collected to eliminate bias. Such data will aid in forming flexible AI models yielding multiple probabilities. Using appropriate and diverse data with no labels and divisions will be useful in eliminating biases.
• Having a diverse range of business problems will create unmanageable classes in the AI models leading to bias. By narrowing the business problems, the AI models will manage and perform effectively without biases and yield revenue.
• Organizations should consider their target audience for their AI models. Selecting your target audience will help in developing an in-depth understanding of the tastes, preferences, races, cultures, and locations of the targeted audience. Such in-depth understanding will further train AI models to develop appropriate insights without bias. These will also bring diversity to the targeted audience.
• Toto eliminates bias, organizations should also look for monitoring performance data of the AI models. Monitoring performance data will point out the loopholes, and weaknesses along with highlighting the areas that need improvement. Monitoring the performance data before production will help in reducing biases.
• The AI models should be equipped with proper feedback options from the opposite end users. Such feedback is important to understand the loopholes and the key areas that need improvement. This feedback when reviewed will help in solving real-world issues that occur in the real world. Such a method will help in understanding the real-life issues and problems faced by the users and eliminate bias accordingly.
• Toto eliminates bias in AI, organizations should set up regulations and guidelines beforehand. The steps of the process should be properly documented and updated whenever necessary. These regulations and guidelines will help in the effective management of the process of eliminating bias.
• Transparency is also crucial in eliminating bias. The whole process of eliminating bias should maintain transparency. Such transparency principles should also be implemented from the beginning of the development of such models.

AI is also being rapidly used in India. From facial recognition to granting loans by using computer models, AI has paved its way into Indian Society and its applications. Although there are no regulatory bodies to manage and eliminate AI bias. There is a need for government-based bodies to regulate the data and eliminate bias from the AI models. This will ensure that there is no further discrimination and will contribute to developing a healthy, diverse yet united society.