It’s interesting to think that AI can express bias. But it’s true; Machine Learning bias, also called Artificial Intelligence bias, can occur when the algorithm delivers biased results. AI systems that generate biased outcomes often mirror and reinforce the existing human biases present in society, which is not beneficial.
The origins of bias within the system are complex; however, there are best practices for those using AI to help avoid biased results. There are also regulations and standards to protect users, which will evolve as technology evolves. In an era where AI shapes decisions across industries, ensuring fairness and eliminating bias in machine learning algorithms is of the utmost importance. We all take responsibility for developing and using systems that perform efficiently and uphold justice and integrity, creating trust in the future of technology.
Understanding Bias in AI
To better understand bias in AI, let’s explore the origins of bias in data collection and algorithm design and its impact on decision-making processes.
Bias in data collection
AI data collection can be biased when the data used to train the AI doesn’t fully represent all kinds of people or situations. For example, if an AI is trained mostly on data from one group (like men instead of women or people from one country instead of a diverse mix), it may learn patterns that don’t work well for everyone.
Biased data can occur for various reasons, including unbalanced data, historical bias, data gaps, and labeling errors. It can also occur at various stages, including the initial training data, the algorithm itself, or the predictions generated by the model.
In an article for IBM, the author states, “When bias goes unaddressed, it hinders people’s ability to participate in the economy and society. It also reduces AI’s potential. Businesses cannot benefit from systems that produce distorted results and foster mistrust among people of color, women, people with disabilities, the LGBTQ community, or other marginalized groups of people.”
Bias in AI needs to be addressed at its core: the algorithm’s design.
Algorithm design
Algorithm design plays a critical role in whether or not AI data is biased. How the algorithm is built deeply affects how it processes and interprets data. Certain design choices can introduce or amplify bias even if the data is unbiased.
If the algorithm selects or prioritizes certain features, like race or gender, inappropriately, it may produce biased outcomes. Features’ skewed weighting or biased data interpretation can also lead to unfair results. Additionally, if the algorithm’s goals focus solely on efficiency or accuracy without considering fairness, it can reinforce existing inequities.
Impact on decision-making
AI bias can significantly impact decision-making processes.
For example, if a biased algorithm is involved in the hiring process within job sectors such as healthcare or law enforcement, it might favor certain groups while disadvantaging others based on race, gender, or socioeconomic status. Unfortunately, this can lead to unequal opportunities and important resources and services.
Strategies for Fairness
Various techniques and best practices exist to reduce bias in machine learning models.
Data pre-processing
In an article from the Journal of Engineering Research, the authors discuss data pre-processing techniques to address data bias. They share four preprocessing methods to remove discrimination in classification problems:
Suppression: Remove protected features and their proxies from the dataset.
Massaging: Change instance labels to reduce disparities and discrimination.
Reweighing: Assign weights to dataset instances to mitigate biases.
Sampling: Use under-sampling and over-sampling to adjust group representation based on protected features, using uniform and preferential sampling.
Algorithm adjustments
In addition, algorithm adjustments help keep AI data fair and unbiased by actively addressing potential sources of bias during the decision-making process.
Algorithms can produce more equitable outcomes by selecting relevant features and excluding biased ones, reweighing data points to balance representation, and incorporating fairness constraints. Regularly testing and correcting for bias ensures that AI systems don’t perpetuate harmful patterns.
Regular audits
Biased AI can lead to unethical outcomes; therefore, businesses and organizations must audit their machine-learning algorithms.
An example of machine learning auditing in the U.S. is the legislation that began in NYC on July 5th, 2023. According to an article, “This law requires employers in New York City to conduct an annual third-party AI “bias audit” of technology platforms they use for hiring or promotion decisions and to publish the audit findings on their websites.”
Although the law applies to one jurisdiction, experts believe other states will soon pass similar or broader legislation requiring AI bias audits. Future laws may mandate audits for age and disability bias, expanding beyond the current focus on gender and race in New York City’s Local Law 144.
Overall, these three strategies in combination will reduce the
The Role of Transparency
Transparency equals increased trust, and with the rise of AI and all of its potential, stakeholders must clearly understand how important decisions are made. However, transparency is more than about building trust; it also helps ensure accountability, helps businesses and organizations align with ethics and laws, and ultimately helps reduce data bias.
Explainable AI (XAI) models are processes that allow users to understand and trust the reasoning behind AI and machine learning decisions.
IBM tech experts say, “It is crucial for an organization to have a full understanding of the AI decision-making processes with model monitoring and accountability of AI and not to trust them blindly. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning, and neural networks.”
A specific example of this is the XAI Decision Tree model.
How it works: Decision trees break down decision-making into a series of choices based on the input data, with each “branch” representing a potential outcome or decision. This structure is easy to follow, allowing stakeholders to trace the reasoning behind a prediction.
Since decision trees clearly show how decisions are made at each step, they are highly interpretable. Stakeholders can see exactly how each input affects the outcome.
For a visualization of this decision tree model, Toward Data Science published a much more detailed example of how this works.
Regulation and Accountability
The US still needs a comprehensive federal law governing AI. However, while AI regulation is still in its early stages, a few noteworthy ethical guidelines and proposed acts to protect users exist.
- The Algorithmic Accountability Act is a proposed U.S. law that would mandate companies to provide transparency about their algorithms and ensure they are fair and free from bias.
- The Blueprint for an AI Bill of Rights This blueprint, by the White House Office of Science and Technology Policy, outlines ethical principles to protect individuals from harmful AI, including the rights to transparency, fairness, and privacy.
In addition to those in the works, several ethical frameworks have been developed to guide AI development and use. These frameworks focus on fairness, privacy, transparency, and accountability.
- Ethical guidelines such as the Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) principles focus on mitigating discrimination and promoting equitable treatment in AI decision-making.
- Regarding accountability, AI developers, organizations, and users must ensure that there is clear responsibility when an AI system causes harm. High-risk AI systems should have human oversight so that humans can intervene if the system behaves unexpectedly.
Published just recently (April 2024) in the JMIR Med Inform journal, experts discuss how research is helping AI fairness, accountability, transparency, and ethics progress.
“Fairness, accountability, transparency, and ethics (FATE) research focuses on evaluating the fairness and transparency of AI and ML models, developing accountability metrics, and designing ethical frameworks.”
The study critically assesses methodologies and practices, identifying successes and gaps while encouraging further innovation to create more ethical and fair AI systems. Key recommendations include using diverse data sets, integrating FATE principles throughout AI development, and improving evaluation metrics to account for all stakeholders. The study has significant implications for researchers, practitioners, policymakers, and the public, advocating for AI technologies that are both ethically sound and technologically advanced, fostering trust and transparency in healthcare applications.
Diverse Data and Inclusive Design
Diverse datasets and inclusive AI design teams are crucial in creating algorithms that reflect a broader range of human experiences and perspectives.
When diverse datasets are used, and inclusive teams are involved, stakeholders and end-users are more likely to feel confident that the machine learning algorithm is fair and representative of their needs.
Inclusive practices also enhance accountability, ensuring that the design and deployment of AI systems are examined by individuals from various backgrounds who can help ensure that the technology benefits everyone.
Conclusion
Eliminating bias in machine learning algorithms is imperative for ensuring fairness and trust in technology in today’s AI-driven world. By using diverse datasets and fostering inclusive design teams, organizations can create algorithms that reflect various human experiences, promoting accountability and transparency. As AI continues influencing key industry decisions, we must prioritize ethical development to build equitable, trustworthy systems for all.