At present, the best AI tools we have at our disposal are considered weak and narrow – they can only accomplish specific tasks using a specific data set for them by their programmers. As a result, artificial intelligence is exceedingly susceptible to various forms of bias that can negatively impact its accuracy and performance.
Bias is a serious issue for anyone interested in building or utilizing AI tools and systems. Identifying and eradicating bias should be the responsibility of every AI user, and these processes begin with understanding more about the causes and effects of bias in AI.
Types of Bias in Artificial Intelligence
Defining bias in AI can be difficult as not every AI expert agrees about the forces that can be considered bias. To some, there are two types of AI bias, algorithmic and societal, whereas to others, there are as many as six types of bias potentially affecting AI.
Largely, bias in artificial intelligence occurs when it is impossible to generalize results widely. Some flaw in an AI algorithm – or a flaw with the data used by the AI, or a flaw in the human understanding of results – prevents results from being accurate or having widespread or practical applicability.
All of the types of bias listed above exist, but most identify biases from different sources within the AI system. Here’s a quick explanation of the most common types of biases recognized by AI experts, which IT professionals working with AI must recognize and monitor for:
Algorithmic/sample/measurement bias. When algorithms are trained using flawed data, they develop biased logical patterns that make all results untrustworthy.
Prejudice/systemic societal bias. When those building or maintaining AI have biases related to their own background – or against those with other backgrounds – they may create biased AI tools.
Representation bias. When a programmer defines a dataset with labels or data types, they can introduce biases by neglecting to represent certain groups sufficiently.
Confirmation bias. When users are eager for a certain result, they may inadvertently program an AI system to produce the result they expect and hope for.
Historical bias. Through history, biases have become ingrained in almost all available data. When AI engineers do not account for these biases, they can continue to impact results negatively.
Evaluation bias. When a model is evaluated and optimized, it is measured against certain benchmarks that can be flawed in their representations of reality.
Aggregation bias. When AI creators combine data populations that are actually quite distinct, they will produce an AI tool that is inadequate at providing appropriate results for all groups.
Eliminating Bias Through Responsible AI
For organizations as well as individuals, biased AI can be exceedingly dangerous. Already, there are dozens of examples of how AI biases have put people at risk in policing and healthcare, and by perpetuating biases that have developed through history, businesses can be liable for continuing to disadvantage certain groups which have already suffered systemic injustices.
It is important that business leaders eager to invest in AI solutions not only understand sources of bias but devise comprehensive solutions for mitigating the effects of biased AI. Responsible AI governance is a concern business leaders must accept before they adopt any AI solutions, which means leaders must take the following steps to reduce bias as much as possible:
Establish organizational principles for AI. A set of ethical principles for AI will help guide creation and use of AI tools across the organization. Examples of valuable principles include: respect for the law, transparency, accountability, human-centered development and security. Leaders should work with their AI team to create principles that are realistic and relevant.
Create and implement a responsible governance framework. Whenever an organization adopts a new form of technology, different department heads must convene to share how they will be involved in the development and use of the tech, which will create a framework to guide the design and implementation of the technology moving forward.
Train the organization in AI bias. Business leaders should enroll in artificial intelligence courses to help them build more knowledge and skill in this relatively new field. The IT team can also benefit from additional education regarding AI bias, and any other staff involved in inputting or analyzing AI data should have bias training.
There are real and serious risks associated with biased artificial intelligence, so anyone creating new AI tools must take pains to understand and avoid biases as possible. Continuous AI training and commitment to responsible AI stewardship could reduce the biases afflicting organizations and their consumer markets.
