Generative AI is in high demand, and for good reason. AI tools are being leveraged to create improvements and efficiencies across industries, such as bettering customer experiences, enhancing data collection and fortifying cybersecurity measures. Financial experts predict that the AI market will reach $400 billion in revenue by 2027, and nearly 75% of executives see AI as the most significant advantage for the future of business.
To ensure the best outcomes from AI, it’s crucial to continue to improve the ethicality of generative AI tools. With so many organizations incorporating generative AI tools into operations, ethical AI adoption can also increase their competitive advantage, simplify tool testing, increase end-user adoption and more.
Watch for bias in AI training data
Training data can be biased, which will lead the AI to produce biased results. This bias can be explicit or implicit, purposeful or inadvertent, but results in skewed responses either way.
Data can often reflect human bias due to societal prejudices, historical inequities, and other biases that are presented during data collection. An AI model is designed to identify patterns to provide relevant outputs. If the model learns from a biased dataset, it is likely to perpetuate that bias.
Humans can also inadvertently transfer their bias to an AI model when training it. For example, if a human with racial prejudice is analyzing demographic data, their inherent bias may lead them to cherry-pick data points that are not representative of a population. This would train the AI model on an inaccurate dataset, making its outputs unavoidably biased.
Promote model explainability and transparency
Two other important ethical AI considerations include model explainability and transparency.
A transparent model can provide better functionality than an opaque model, as it provides users with explanations for its outputs. An opaque model does not need to explain its reasoning process, which introduces risk and potential liability if unexpected or inaccurate results are provided by a generative AI tool. This lack of visibility also makes opaque models more difficult to test than their transparent counterparts. As such, it’s important to consider generative AI tools with high transparency when working to build ethical systems.
Explainability of AI models is another important aspect of creating ethical systems yet challenging to control. AI models, specifically deep learning models, use thousands upon thousands of parameters when creating an output. This type of process can be nearly impossible to track from beginning to end, which limits user visibility. Lack of explainability has already been demonstrated in real-world problems; we’ve seen many examples of AI hallucinations, such as the Bard chatbot error in February 2023, which occurs when a model provides an output that is entirely false or implausible. A lack of explainability can contribute to hallucinations by preventing developers from identifying and solving a model’s root problem.
To understand why hallucinations or similar phenomena occur, it’s crucial to build transparency into AI models from the start. Knowing what data an AI model utilizes, where it came from and how the model was trained can help avoid problems with explainability and transparency, and ultimately contribute to a more ethical AI landscape.
Addressing ethical concerns through design & data usage
To mitigate these issues, technology leaders and AI creators can focus on two key principles to develop ethical and secure models:
1. Privacy by design
Privacy parameters can be implemented throughout the entire process of AI model design, including the selection and treatment of data and the type of model and algorithm used. Differential privacy also allows developers to enforce statistical guarantees on the level of privacy provided to different pieces of data. By incorporating privacy parameters across each of these areas, both users and developers of generative AI tools can remain protected.
Additionally, by building privacy into the model design, technology leaders can better avoid data breaches and attacks resulting from AI model weaknesses.
2. Data minimization
Data that you don’t have is data that cannot be stolen. As such, another key element to building ethical AI systems involves limiting data usage as much as possible. For example, AI model developers can exercise feature engineering and dimensionality reduction, and they can also try to select algorithms with fewer collection columns to avoid overuse of data.
Minimizing data collection use improves the overall ethicality of AI tools and reduces the risks of data breaches and theft.
As AI continues to transform how we do business and live our lives, we must confront the ethical dilemmas involved in its creation and use, including potential bias. By following these development principles, educating employees on the limitations and dangers of AI, and thoroughly vetting the AI tools we use, we can build more ethical models and improve their security for all.