Why Discrimination and Misinformation is Inherent in AI | Arvig Blog Skip to main content
Reading Time: 8 minutes
Blue AI on Equipment

Why Discrimination and Misinformation is Inherent in AI

And How We Can Make AI Better

Artificial Intelligence (AI) is part of our technical revolution, but also has a tremendous problem with bias. Here are some examples:

  • An AI bias in the US court system incorrectly predicted twice as many black offenders would repeat their crime than while offenders.
  • A hospital algorithm used on over 200 million people heavily favored white patients over black patients in predicting who would need additional medical care.
  • Tech giant Amazon realized their algorithm used for hiring employees was biased against women. 

The FTC warns discrimination, bias and misinformation can be inherent in AI because they reflect the biases and limitations of the data and algorithms used to train and operate AI systems.

AI systems are only as good as the data they are trained on. If the training data is biased or incomplete, the AI system will learn and reinforce those biases. 

AI algorithms themselves can also contain biases. These biases can stem from the assumptions and values of the individuals or organizations that develop and program them.

Because of the massive volume of misinformation that already exists on the internet, AI is struggling to sort out real or false information.

Discrimination Issues
Discrimination in AI can cause significant problems for minorities in several ways. Here are some examples:

Biased facial recognition: Facial recognition technology has been less accurate for people with darker skin tones and for women. This can cause higher rates of false positives and false negatives, leading to inaccurate identification and potential harm for minorities.

Discriminatory hiring algorithms: AI-powered hiring algorithms have been found to be biased against women and minorities. For example, if an algorithm is trained on data that is biased against certain demographics, it may favor candidates from certain schools or with certain backgrounds, leading to perpetuation of inequalities in the workforce.

Unequal access to credit: Credit scoring algorithms can perpetuate discrimination by relying on biased data that disproportionately affects low income and minority borrowers. For example, if an algorithm uses data such as zip codes or education levels to determine creditworthiness, it may unfairly penalize individuals from marginalized communities.

Two people working together on a computer

Biased healthcare decisions: AI-powered healthcare systems have been found to perpetuate discrimination by relying on biased data or algorithms, reinforced by healthcare worker’s own biases. For example, if an algorithm is trained on data that has a limited data set, it may bias the results against certain demographics, it may not accurately diagnose or treat certain conditions, leading to potential harm for patients.

Racial profiling by law enforcement: AI-powered law enforcement technologies, such as predictive policing or facial recognition, can perpetuate discrimination by targeting minorities based on biased data or algorithms. This can lead to unfair treatment and harm to individuals, including human rights violations. 

These examples show how discrimination in AI can cause significant harm to specific sectors of our population.

Steps to fix discrimination in AI
To address these issues, it is important to develop and use AI systems that are transparent, accountable, and designed with diversity and inclusivity in mind. This includes using diverse and representative data, designing algorithms that are transparent and can be audited for bias, and ensuring that AI systems are subject to ethical and regulatory oversight.

Here are some steps to fix discrimination in AI:

Diversify and improve the quality of data: They are biased. One of the primary causes of discrimination in AI or incomplete training data. It is crucial to ensure that data used to train AI models is diverse and representative of all groups. Efforts can be made to collect more data on underrepresented groups and to remove any biases from existing data sets.

Evaluate and address algorithmic bias: It is important to design algorithms that are transparent and auditable, and that do not perpetuate discrimination. Developers can conduct regular audits of AI systems to identify and address any biases.

Establish ethical guidelines: It is important to establish ethical guidelines and codes of conduct for the development and use of AI. These guidelines should address issues such as fairness, accountability, transparency, and privacy.

Involve diverse stakeholders: It is important to involve a diverse group of stakeholders, including individuals from underrepresented groups, in the development and deployment of AI systems. This can help ensure that systems develop with diversity and inclusivity in mind.

Misinformation can be inherent in AI if the algorithms used to process information don’t account for uncertainty or ambiguity. For example, if an AI system is designed to classify news articles as either true or false, but the articles contain complex, nuanced information, the system may struggle to accurately make this distinction, and may inadvertently perpetuate misinformation.

Misinformation in AI can have a significant negative impact on our society in several ways:

Spreading false information: AI-powered systems whose purpose is to generate content or automate the dissemination of information can spread false information quickly and at scale, which can have serious consequences. This can lead to the spread of misinformation, propaganda, and conspiracy theories, which can further polarize and divide society.

As an Amazon Associate, Arvig earns from qualifying purchases.

Undermining trust in institutions: Misinformation in AI can erode trust in institutions and public figures, leading to decreased confidence in government, media, and other sources of information. This can make it more difficult for institutions to effectively communicate with the public and address important issues.

Reinforcing biases and stereotypes: AI-powered systems that are trained on biased or incomplete data can perpetuate existing biases and stereotypes, which can further marginalize and harm certain groups. For example, if an AI system is trained on data that is biased against certain demographics, it may perpetuate stereotypes or reinforce negative attitudes towards those groups.

Facilitating malicious activities: Misinformation in AI can be used to facilitate malicious activities, such as phishing attacks or social engineering campaigns, which can harm individuals and organizations. For example, an AI-powered system that generates convincing fake news stories could spread malware or steal personal information.

Creating confusion and uncertainty: Misinformation in AI can create confusion and uncertainty, making it difficult for individuals to make informed decisions or take appropriate actions. This can be particularly problematic in situations where accurate information is critical, such as during a public health crisis or emergency.

These are just a few examples of how misinformation in AI can harm our society.

Steps to eliminate misinformation in AI
Eliminating misinformation in AI requires a multi-pronged approach that involves improving the quality of data used to train AI systems, designing algorithms that can handle uncertainty and ambiguity, and promoting transparency and accountability in AI systems. Here are some steps that can be taken to eliminate misinformation in AI:

Use high-quality data: How well an AI system turns out depends on inputs of good data. To eliminate misinformation, an AI system needs high-quality data that is accurate, comprehensive, and up-to-date. Data should be sourced from multiple reliable sources to minimize the impact of bias or errors.

Train algorithms to handle uncertainty: AI systems should be trained to handle uncertainty and ambiguity. This can involve designing algorithms that can identify and flag uncertain or ambiguous data points, or that can work with probabilistic models to provide more nuanced results.

Implement fact-checking mechanisms: AI systems can be trained to fact-check information and identify potential sources of misinformation. This can involve using machine learning algorithms to identify patterns and inconsistencies in data, or leveraging human oversight to verify information.

Promote transparency and accountability: To eliminate misinformation in AI, it is important to promote transparency and accountability in AI systems. This can involve making the data and algorithms used to train AI systems publicly available, providing detailed documentation on how AI systems operate, and implementing mechanisms for users to report and address inaccuracies or biases in AI outputs.

Additional Solutions to Combat Discrimination and Misinformation
These important steps apply to both discrimination and misinformation in AI:

Ensure regulatory oversight: It is important to establish regulatory oversight of AI systems to ensure that they are being used ethically and that they do not perpetuate discrimination or misinformation. Regulations should be established to govern the collection, storage, and use of data in AI systems, as well as to ensure transparency and accountability in the development and deployment of AI.

Regularly monitor and update AI systems: It is important to regularly monitor and update AI systems to ensure that they are functioning as intended and are not perpetuating discrimination or misinformation. This can involve regular testing and evaluation of the systems, as well as ongoing data collection and analysis.

By taking these steps, it is possible to reduce and ultimately eliminate discrimination and misinformation in AI, and to ensure that these systems are designed and used in an ethical and inclusive manner.

Related Posts