As generative AI continues to infiltrate and disrupt every business sector, from customer service to content creation, concerns are understandably growing about its potential risks, especially around bias and fairness. Because, although this technology can clearly unlock efficiencies and deliver business benefits, there are also a host of potential challenges that need to be addressed regarding equity and transparency before it can be implemented at scale.
Understanding bias
The conversation around potential bias in AI started long before the arrival of ChatGPT or the mainstream media discussion of Large Language Models (LLM). For as long as there have been AI algorithms, there has been concern that due to decisions made during their training, design and development, these algorithms could act with favoritism or prejudice. For generative AI, this bias can manifest in several ways:
Training data
The quality and quantity of data used to train an AI model will have a direct influence over its potential for delivering biased results. For instance, if it is trained exclusively on English-language sources, outputs could be perceived to reflect a western bias. Or, if a model was trained on a much wider geographical sample of data but each piece of training text pre-dated 2001, outputs could appear to reinforce or prefer outdated societal or political views.
Algorithm design
The algorithms themselves can introduce bias if they are not designed with fairness in mind. For example, choices made regarding how to weigh different types of input data can create an imbalance that favors certain outcomes over others, irrespective of the training data.
Feedback loops
Generative AI relies on user feedback to judge, confirm and improve its outputs. If the feedback mechanisms aren’t properly weighted or tested for fairness or objectivity, then they can create and reinforce inequalities in the model during and after development so that its outputs are biased.
Transparency issues
Generative AI is capable of delivering complex and comprehensive outputs but many of the most popular systems are opaque insomuch as they do not or cannot explain how they arrived at their answer or solution. And this lack of clarity and transparency can lead to a lack of trust and can compound issues around bias and fairness in a number of ways.
Interpretability
As many AI systems are essentially black boxes where a question goes in and an answer comes out but the calculations to arrive at the response are hidden, it makes it even more difficult to identify if a model is biased and in which regard. And if users can’t look under the hood, so to speak, they are going to be less likely to trust it or its outputs.
Accountability
And of course, if there is no way of understanding the decision-making process, it becomes difficult to pinpoint where the problem lies — in the training data, model design, user input or initial calibration through feedback mechanisms. Without knowing who or what is accountable, it is difficult to develop or establish a governance structure for the system’s use within an organization.
Questioning fairness
While it can be argued that fairness and bias are very similar, fairness becomes a distinct issue when GenAI is applied within a specific role or field. For instance:
Content moderation
When used for content moderation, generative AI tools can disproportionately censor or silence certain opinions or perspectives simply because the training data is not representative of the full customer or user base.
Recruitment
If a model used for applicant screening or selection is trained on historical data that reflects historical societal bias or inequality, it is likely to favor certain demographics over others and this runs the risk of making the recruitment process unfair.
Creativity
In the creative industries, music art, design and literature generated by or with support from GenAI can raise questions about authorship, originality, and the potential homogenization of creativity. The blending of existing works may in the short-term lead to claims of plagiarism or theft and, in the long term, reduce the diversity of creative output, ultimately affecting artistic expression and cultural narratives.
5 ways to overcome bias and fairness issues
Once organizations are aware of the issues and their root causes, there are several ways to mitigate potential biases, build trust, ensure fairness and obtain the necessary buy in so that generative AI-powered tools and solutions can deliver genuine business benefits.
1. Diversified data
One of the best ways to address bias is by improving or augmenting training data so that it has a greater breadth and depth and is more representative and reflective of the world and the context in which the technology is going to be deployed. Organizations should:
- Conduct data audits: Regularly assess the datasets used for training generative AI models to identify potential gaps in representation.
- Incorporate diverse sources: Actively seek out diverse data sources to include in training.
- Augment data with synthetic samples: In cases where data is limited for certain demographics, consider using synthetic data generation techniques to create balanced datasets.
2. Algorithm design
Organizations can employ specific techniques during the algorithm design phase to minimize the introduction of bias. These include:
- Fairness constraints: Implement fairness constraints within models to actively counteract known biases. This involves designing algorithms that take into account multiple fairness criteria and ensure equitable outcomes.
- Regular testing for bias: Develop testing protocols that simulate diverse user interactions to detect and quantify biases in model outputs. Use tooling like bias detection frameworks to analyze the results and measure effectiveness.
3. Transparency and interpretability
Improving transparency is crucial for building trust in AI systems. Organizations can take the following steps:
- Model explainability: Invest in explainable AI (XAI) tools that help demystify the decision-making processes of generative models. Providing stakeholders with insights into how and why outputs are generated fosters greater understanding and accountability.
- Documentation and disclosure: Maintain thorough documentation of the development process, training data sources and assumptions made during algorithm design. This information should be accessible to internal stakeholders and serve as a resource for external audits.
- User feedback mechanisms: Implement feedback channels that allow users to report concerns or issues regarding bias or fairness in AI outputs.
4. Promote inclusive and ethical AI development
Teams that identify use cases and develop and test solutions should be multidisciplined and representative of the organization, rather than limited to members of the IT department. As well as increasing the capacity for creative thinking, a diversified team will be better at identifying potential opportunities and potential biases in solutions as they are developed.
At the same time, provide training to the wider organization that focuses on ethical use of AI, bias awareness and transparency so that all employees are equipped to identify, address or report biases or potential issues as they encounter them in their work.
5. Continuous monitoring and evaluation
Bias mitigation and fairness promotion should be ongoing efforts rather than one-time initiatives. Organizations can:
- Establish regular reviews: Conduct periodic assessments of generative AI outputs to identify any emergent biases or issues related to fairness. Consistent evaluations will help organizations stay attuned to the dynamic nature of AI interactions.
- Adapt and iterate: Use outputs from monitoring reviews and user feedback to iteratively improve algorithms and processes. Being flexible and adaptive can lead to more robust and equitable AI systems over time.
As generative AI continues to evolve and integrate into diverse facets of society, addressing bias and fairness must remain a priority. Organizations need to recognize and understand the areas and processes where biases can seep in and be proactive in developing the right approaches to ensure their use of AI is accountable and ethical. Only then can they win their employees’ and their customers’ trust and truly unlock this technology’s benefits.
To learn more about how Generative AI will shape the future of customer experience delivery and the challenges businesses will need to overcome to truly unlock the technology’s benefits, read: From buzzword to business case: The 2025 CX Trends Report.