Leveraging Empathy to

Reduce Bias in AI

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Introduction

While the world around us seemingly races to implement the next great innovation in personalized services and experiences, Artificial Intelligence (AI) systems have quickly risen to become the enabling technology of choice. From personalized recommendations while online shopping to “intelligent” virtualized support agents, it can feel like robots are, in fact, taking over the world.

While many times they leave us with an uneventful or even good experience, what happens when the programming fails and delivers us a negative or, even worse, an insulting one? Alongside their potential benefits, AI systems also introduce risks, particularly in decision-making processes. This brings us to the question, how can we make our Artificial Intelligence have more Emotional Intelligence?

Bias in The Machines

What makes it possible for a machine to have a bias at all? To better understand how this can occur, let’s first understand the difference between traditional or legacy system design compared to newer AI Systems. For much of our modern era, the systems and programs we interacted with were created by programmers using decomposition. In this formulated method, the desired problem or solution statement was broken down or “decomposed” into smaller, more manageable components focused on a subset of the problem, for example, requesting the inventory on hand or calculating the sales tax on an item within a retail catalog. These components could then be tied together in a functional process to create complex patterns for solving the problem, such as the customer checkout flow.

While traditional programming focuses on explicit instructions and logic, AI emphasizes the development of systems capable of learning and adapting, which combines logic blocks with inferencing and pattern matching across large data models to create responses based on scoring and other methods of weighing the potential answer.

Although both approaches aim to solve problems and automate tasks, because they do so through fundamentally different methodologies and principles this is where bias can be introduced inadvertently when it comes to a traditional “emotionless” computer system. Much like the human personas AI is envisioned to imitate and mirror, AI can develop its own personalities, leading to the manifestation of bias in several forms, including racial, gender, or socioeconomic biases, leading to unfair or discriminatory outcomes. These biases are typically a reflection of societal influences embedded in the data used to train AI models and possibly the experiences of the programmer. Moreover, these systems can also be at risk of becoming more skewed in favor of certain outcomes due to flaws in their design or the data feeding them.

Risk bias in the context of AI refers to the tendency of Artificial Intelligence systems to make decisions that systematically favor certain outcomes or actions over others, often without considering the full spectrum of potential risks involved.

Types of Bias

As discussed in a 2022 report from the NIST, bias within systems is derived from three main channels or vectors.

  • Statistical: Datasets utilized during model training and analysis input sources where a group or dimension of data is excluded from the model, for example, a demographic or geographic region.
  • Human: Derived as a result of the individual(s) developing the model and algorithms and how they select to fill in gaps of information and or weight the scoring systems.
  • Systemic: This results from social institutions, such as the research facility or engineering lab, creating the models where a specific viewpoint or culture
types-of-bias

Bias within the machines can manifest in various forms depending on the specific application and context of the AI system:

  • Overestimation or Underestimation of Risks:
    Variances in the risk calculated can be grossly above or below the actual severity of certain risks as a result of the data they are trained on. For example, the AI might overestimate the risk of default for individuals from certain demographics due to biased training data.
  • Ignoring or Undervaluing Certain Risks:
    Failure to adequately account for certain risks that are not well-represented in the training data. Decisions that overlook important factors or potential consequences can result in increasing overall risk.
  • Consistency in Biased Decision-Making:
    Consistency in biases during decision-making can result in certain groups being consistently favored or disadvantaged based on misaligned datasets. This can perpetuate inequalities or unfair treatment across different applications of AI.
  • Algorithmic Design and Deployment Bias:
    Bias can also arise from the design and deployment of AI algorithms themselves. Factors such as the choice of features, data preprocessing techniques, and model architecture can inadvertently introduce biases that affect decision outcomes.

Addressing risk bias in AI involves a combination of technical measures (such as improving data quality, designing fairer algorithms, and implementing bias detection tools) and ethical considerations (such as ensuring transparency, accountability, and inclusivity in AI development). By proactively identifying and mitigating risk biases, developers can enhance the reliability, fairness, and societal acceptance of AI technologies.

The Role of Empathy in Mitigating Bias

Empathy In Development

It is predicted that there could be up to 800 million workers replaced by AI-powered systems and automation by 2030. As a result, coding for these robots and programs will be a major area of focus for technologies to meet the high demand.

empathy-in-development

Empathy embedded into AI will help pave the way for the systems to thoroughly interact with and understand people based on what they mean, rather than just absorbing what they say. In general nature, we as humans do not necessarily cover all details when they share stories and other information. Sometimes, depending on the environment or even the emotional state that they are in, they may withhold information out of fear, distrust, or another inhibiting factor due to internal and external factors.

For example, when with peer groups, an individual may not share all details of a work problem being faced as it could be too personal to address, or the inflection in a person’s voice may make certain words or statements be identified and inferred incorrectly as they may express themselves in a less than articulate way. As systems designers develop the algorithms and models, they should consider these and ensure they accommodate where possible for the potential to:

  • Make sense of what is not being said, or what is being hinted at beneath the external expressions and words
  • Develop ways to mimic intuition, imagination, emotional sensitivity, and creativity to drive a deeper engagement with audiences
  • Collect and define the right data and data models to extract the right kinds of insight

Practical Applications of Empathy in AI Development

- Diverse Stakeholder Engagement:

Engaging diverse stakeholders, including individuals from different demographic groups and domains of expertise, allows developers to gain insights into potential biases that may be embedded in the data or algorithms.

- Ethical Considerations:

Integrating empathy into AI ethics frameworks ensures that developers prioritize fairness and equity when designing and deploying AI systems. This includes establishing guidelines for handling sensitive data and ensuring transparency in decision-making processes.

- Bias Detection and Mitigation:

Empathy-driven approaches can enhance the ability to detect and mitigate biases during the AI development lifecycle. Techniques such as bias auditing and fairness testing can be informed by an empathetic understanding of how biases affect different groups.

Case Studies and Examples

- Healthcare:
AI algorithms used in healthcare diagnostics must account for diverse patient populations to avoid diagnostic bias based on demographic factors.

- Commerce:
Dynamic pricing that is skewed incorrectly between different geographical

- Finance:
Empathetic AI in finance can mitigate biases in loan approval processes by considering individual circumstances rather than relying solely on credit scores.

Conclusion

In conclusion, leveraging empathy in AI development is essential to prevent and mitigate risk bias. By embracing empathy, developers can create AI systems that are more inclusive, fair, and aligned with ethical standards.

Additionally, while AI continues to evolve, the integration of empathy will enhance the accuracy and reliability of AI systems while also ensuring they positively impact society as a whole.

Future Directions

Future research should focus on further integrating empathy into AI systems through open collaboration and the development of frameworks that help drive adoption. This addition will hopefully not only enhance the willingness to trust AI technologies but also contribute to a more equitable and socially responsible AI ecosystem.

By harnessing the power of empathy, the AI community can proactively address bias, fostering a future where AI technologies contribute positively to society while minimizing harm and discrimination.

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References:

https://hbr.org/2024/05/ais-trust-problem

The Difference Between Generative AI And Traditional AI: An Easy Explanation For Anyone (forbes.com)

There’s More to AI Bias Than Biased Data, NIST Report Highlights | NIST

Coding And Empathy: Foundational Skills For The Future Of Work (forbes.com)

What Is Empathy and Why Is It So Important in Design Thinking? | IxDF (interaction-design.org)

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