The potential of artificial intelligence is very high. The tool has become one of the technological solutions with the most potential and one that the IT managers of companies trust to solve many of the corporate challenges. After all, AI can achieve things as varied as creating works of art or anticipating accidents and improving road safety.
But despite the high potential of artificial intelligence, there are also a few critical voices warning of what could be a serious problem. AI must be very careful not to fall into algorithmic bias. Algorithmic bias leads artificial intelligence to reach problematic conclusions that do not truly represent the world in which it operates, which has already dragged some companies into a reputational crisis because of it. It happened to Google not too many years ago, for example, when it’s AI could only efficiently identify people of color.
The negative impact of algorithmic bias affects corporate reputation and the company’s position in the market. In The invisible woman, the researcher Caroline Criado-Perez demonstrates how companies forget women in designing their products, which means a loss of opportunities.
For all these reasons, companies must strive to prevent their artificial intelligence from being affected by bias and make it as neutral as possible. Achieving it is possible: they must trust the most appropriate technology and bet on the most successful IT practices.
How To Avoid Bias In AI
The same regulations emerging to regulate artificial intelligence are already creating environments to make AI more ethical and neutral. Thus, for example, the rules of the European Union already create a governance framework for artificial intelligence. Even so, companies can go much further than what the regulations indicate.
To begin with, it is very important to have a clear internal policy for AI and data management. Data governance is the basic piece of this work since it creates an unquestionable data policy that applies to all corporation departments and clarifies what should be done and whatnot. It is the pillar on which the company’s AI work will rest.
To proceed, companies themselves need to be acutely aware of the problems that algorithmic bias can create and work to avoid it. One of the most important ways is to create a varied and rich database.