There is no denying that Artificial Intelligence (AI) has become an important part of every aspect of our lives from making better recommendations to aiding strategic decisions in sectors such as finance, healthcare, and even law enforcement. However, AI is not infallible and one of the main obstacles it is confronted with is bias. Bias in AI arises when an algorithm reaches conclusions that are biased due to incorrect assumptions or unbalanced information, resulting in consequences that are often unjust or even damaging. Through this blog, we will explore what AI bias is, how it comes about and examine the need to fix it.
What Is AI Bias?
AI bias wages war against certain target groups or individuals through algorithms which make decisions or predictions. An example of bias that might affect individuals is a societyโs outlook toward females and males. Such bias cannot be construed as AI because its causes are traced to the data that such models are trained upon. Such a compensation philosophy might lead to biased and unfair algorithms whenever the data used for such policies contain preexisting social stereotypes.ย
For instance, a recruitment AI that is built on data which has been used in the past to recruit people may end up learning that female candidates are to be compared poorly with male candidates and this could be due to bias in historical recruitment practices thus choking the cycle of gender inequality.
Forms of AI Bias:
Data Bias: Occurs when the data used for training is limited and therefore fails to explore the demographic territory adequately
Algorithmic Bias: Takes place in the case that some cross-section of the operating parameters of the AI exhibits bias within the process of making a choice or designing a model.ย
Selection Bias: This type of bias comes into play if the range of data collected does not include a representation of the relevant broader audience.
What Causes Bias in Artificial Intelligence?
Because AI models process a lot of data, any problem in that data will result in problems in the outcomes. Among the many reasons for AI bias, the following are the most common:
Bias from History: Often learns AI system on the data that contains some biased decisions in the past, and as a result, such system learns to be biased too. For example, if in the past credit decisions discriminated one group from another, the AI model may do the same.
Selection Bias: In cases when the training set of an AI system does not reflect the real population, it is possible that some groups do well while others do not. For example, face recognition systems which are developed and trained predominantly on light-skinned data sets are famous for their inaccuracy for dark-skinned faces.
Inherent Bias: Such bias is introduced through the incorrect assumptions made in the collection of data. For example, in financial modeling the application of zip codes to determine economic status can be problematic bearing in mind that certain codes may be captive to particular races.
Instances of AI Bias in Practiceย
AI bias has already influenced some practices significantly and it has enough examples that show the risks it can pose when taken lightly:ย
Facial Recognition and Racial Bias: Performance of facial recognition systems varies significantly across ethnic lines. Numerous studies have shown that the majority of existing facial recognition systems perform very poorly on images of people with dark skinned faces. This can be very problematic, especially when such systems are incorporated into other processes like law enforcement agencies.ย
Healthcare Algorithms: In a study conducted in 2019, it was established that a commonly used health care algorithm was flagged as being racially biased towards Black patients which resulted in an inferior quality of care recommendations for the patients most in need. There was bias in the algorithm since the authors of this data utilized healthcare costs to measure the need for care. Resulting in historical analysis where care costs recorded for black patients has always been low due to lesser healthcare access.
Job Recruitment: Several organizations have adopted the use of AI systems to assist in the evaluation of individuals applying for jobs. Yet, if the AI is built with the historical data of selection and training of people from certain ethnic of gender backgrounds, these systems are likely to introduce the same biases in future recruitment. Amazon infamously abandoned an artificial intelligence recruitment program when it realized that the machine learning model was discriminating against women. The reason was that the program was fed with resumes exceeding ten years ago that were predominantly from men.
The Dangers Of AI Discrimination
AI discrimination is dangerous because these systems are often used to inform decisions that have real-life consequences. Hereโs why it is essential to fight AI discrimination:ย
To promote fairness and equality: It is expected that AI systems will encourage fairness and not exacerbate the existing one. However, this is a known limitation of such systems, as they can contribute to the wider societal inequality in aspects such as employment and health services.
Tackling the perception of AI systems as bias: People will very rarely trust an instance of AI if the outcome delivered to them is perceived to have discrimination or unfairness embedded in it. This helps explain the sluggish pace in the acceptance of AI systems and the advantage they are meant to bring. For successful integration of AI systems acceptance by users is shaped by the development of appropriate mechanisms that ensure fairness and justice in the systems.
Limitation of AI Systems โ Policy and ethical: Most nations have started developing polices that advocate for fairness in the usage of artificial intelligence, especially in areas such as health and finance. Corporations that employ discriminatory artificial intelligence systems put themselves at risk of incurring legal liabilities and incurring adverse public relations.
Impacts on society: When binary ai is used, there is a risk that it can bridge social gaps or create new ones. This increases the possibility of pushing the already at-risk populations to the periphery. That is why it is crucial to focus on the problem of bias. It is important that such technology does not discriminate and is available to all.
What Approaches Can Be Used To Modify AI Bias?
Most likely it would be too optimistic to expect the removal of bias. Yet still there are some techniques aimed at reducing bias in AI models:
Inclusive and Varied Data: The incorporation of a well balanced representative data set is one of the most effective methods in minimizing bias. AI systems can then be trained on datasets that represent the healthy mix of the general population.
Bias Detection and Testing: Performing regular checks is necessary to detect any possible biases that may exist in the AI systems. Disparities among ethnic groups can also be revealed and rectified through implementing such strategies.
Open-Source Algorithms: Making AI systems of development non-biased to discrimination helps to eliminate the esoteric nature of any system and revealing the basics of decision making by an AI system. Explainable AI (XAI) is a new research area that strives to make the machines which have algorithms more understandable.
Human Intervention: Presumably it has been advanced to an extent where it may be used without human intervention. The unusually creative perception in most cases comes from people who have or are simply commissioning diverse groups of individuals to monitor the use of an AI system.ย
Example:Google is using a strong fairness evaluation that includes testing AI models for bias and using different people to examine those models for fairness.
Final Thoughts: Why Tackling AI Bias Should Be a Priority
AI has the potential to bring about many positive changes to our society but only if it is supported by high quality data and well defined processes. Elimination of AI bias is crucial for development of fair, egalitarian and efficient AI systems and technologies that are meant for all communities. As it is the case with developers, businesses and governments who are embracing the use of AI, the principles of fairness and transparency shall be indispensable in ensuring that these developments are made to benefit the society without discrimination.
With the growing application of AI comes the great responsibility to track and manage the bias. By managing the risk of understanding why AI can be biased, we can build a future where AI presents a more positive change for every member of the society instead of amplifying existing social fault lines.
