Overcoming bias in AI

Artificial Intelligence (AI) has rapidly integrated into various aspects of our daily lives, affecting not only our personal lives, but also the way we work. At Enhesa, we’ve combined AI’s transformative power with the prowess of our talented legal experts to help unlock the immense value embedded in the content our analysts carefully create. As these systems become more widespread, concerns about bias in AI have understandably surfaced, raising critical ethical and social questions.

Bias in AI can manifest in numerous ways, often reflecting and amplifying existing societal prejudices. This can lead to unfair treatment of individuals based on race, gender, age, or other characteristics, perpetuating discrimination and inequality. The root causes of AI bias are multifaceted, with various aspects involved, such as:

• Biased data sets
• Flawed algorithms
• A lack of diversity among those who design the systems
• A lack of human supervision

To create fairer AI systems, it’s essential to adopt comprehensive strategies that include diverse data collection, algorithmic transparency, and inclusive and diverse development teams. Moreover, ongoing monitoring and regulation are crucial to ensure that AI systems evolve in ways that promote equity and justice.

At Enhesa, we’re aware of the challenges associated with AI bias and the need for fairness in technology adoption. We’re committed to ensuring that our practices reflect these values, striving for equitable and just results in all our AI initiatives. In this article, we’ll outline some of the strategies and measures we employ to address and mitigate these issues.

What is bias in the context of generative AI?

Generative AI involves advanced artificial intelligence algorithms capable of producing human-like text by leveraging vast amounts of training data and deep learning techniques. It excels in tasks such as content creation, question answering, and language translation. However, its “understanding” is derived from statistical patterns rather than genuine comprehension. Although large language models (LLMs) present a valuable potential for the evolution of computing and its numerous applications to aid humans in a diversity of fields, concerns have been raised from multiple angles, including the opacity of the system’s operations, its environmental impact, and its biased tendencies. Given the vast number of parameters and size of the training datasets used, these models are increasingly more challenging to curate.

Bias is a concept often used in machine learning to identify unfairness in model outputs, but specifically unfairness related to social groups and socially driven forms of discrimination. In the context of LLMs and generative AI, this means the replication of these forms of reasoning within the output of a language model. Social biases are examined on the basis of formulated prompts, which replicate hegemonic social understandings.

Lowering bias in AI

Efforts to reduce AI bias focus on creating fairer algorithms and higher quality data collection. It’s not just the performance of the algorithm that’s important, but the combined process and outcome of both the AI and the person supervising it. This approach not only better reflects the reality that most AI systems are currently supervised by humans but also offers a means to mitigate bias in AI.

At Enhesa, not only do we have an in-house team of AI engineers, but they also work together with our regulatory experts to ensure there’s always a human in the loop — both in algorithm development and reviewing the outcome of them. For many of our projects, we prefer and tend to use our own internal content as data. This way we can assure that the machine is learning only from data that’s been carefully curated by our legal experts and — in the case of machine translation — trained translators. This synergy guarantees that human oversight is in place, allowing us to consistently deliver the unparalleled accuracy and precision our clients have come to depend on.

In addition to that, and as we work with many different languages, we also consider language differences that can cause bias, such as in gendered languages like Spanish, and we pay special attention to this during the training stage. We’re also aware of different language varieties within a language and develop specific models for them to get the correct results — like the nuanced differences between Portuguese and Brazilian Portuguese.

Robust policies for AI ethics and generative AI use

Our day-to-day work is guided by our AI policy. We’ve carefully developed both a Generative AI Use policy and an AI Ethics policy — the latter being based on the Ethics Guidelines for Trustworthy AI from the European Commission’s Independent High-Level Expert Group on AI.

These policies mean that, for all our projects, we consider respect for human autonomy, prevention of harm, fairness, explicability, and the source of our training data. Moreover, it allows us to set clear boundaries on the applications of AI. High-risk use cases, like hiring processes, can be identified and explicitly excluded from AI usage.

Explainaibility

Explainability in the context of AI refers to the ability to understand and interpret the decisions and outputs generated by AI systems, clarifying how a model arrives at its conclusions and making the underlying processes transparent and comprehensible. This transparency is crucial, particularly for complex models like deep neural networks, which often operate as “black boxes” with decision-making processes that are opaque and difficult to decipher.

Explainability entails providing clear insights into the model’s functioning, including the data it was trained on, the features it considers important, and the reasoning behind its predictions. This not only promotes trust and accountability but also enables the identification and correction of biases and errors within the AI system. By making AI more understandable, we can ensure its decisions are fair, reliable, and aligned with ethical standards.

At Enhesa, we ensure that our AI systems not only deliver high accuracy but also significantly reduce the likelihood of false positive outcomes by prioritizing transparency and understanding in our models. This focus on explainability allows us to quickly identify and rectify any issues, maintaining the reliability and integrity of our solutions. Our clients benefit from AI that is not only powerful but also accountable, providing them with the confidence that our technology meets the highest standards of precision and trustworthiness.

AI is a new, fast-developing area of technology, which is why we believe it’s vital to stay at the forefront of the latest scientific and academic research on AI. In the case of explainability, we apply and replicate advanced metrics to analyze and understand the reasoning behind our models’ predictions. This commitment ensures that we maintain transparency, build trust, and continuously improve the performance and fairness of our AI systems.

What’s next for Enhesa’s AI?

As we continue to navigate the evolving landscape of AI, our focus remains steadfast on addressing and mitigating biases while enhancing explainability. The journey toward fair and transparent AI systems is ongoing, and we’re dedicated to staying at the cutting edge of research and innovation. We’ll continue expanding our explainability frameworks, ensuring that our models are not only accurate but also transparent and understandable.

This commitment means we provide our clients with access to AI solutions that are trustworthy, accurate, and fair. Enhancing our models’ transparency enables more informed decision-making and fosters greater confidence in AI technology, while our policy-led dedication to ethical standards ensures that our AI systems deliver precise and equitable outcomes.

Learn more about how Enhesa uses AI

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