Building Responsible AI
We are in an AI-driven era, which is rapidly evolving and brings new aspects to our daily lives. However, it’s crucial to make these solutions accessible and universal for all, especially for the disabled community.
According to the World Health Organization (WHO), over 380 million working-age adults live with disabilities around the globe, and unemployment rates among them are reaching up to 80% in several regions. Currently, AI solutions are used to improve the lives of people with disabilities; however, fairness issues for people with particular classes of disability are often overlooked in training and evaluation data used for AI development. This creates a scarcity in disability data and becomes a major barrier to building an inclusive and responsible AI.
Data is the backbone of any AI solution, but most AI models are trained with existing datasets that lack representation of diverse groups. Recently, a report highlighted that the Center for Democracy and Technology has warned that the lack of high-quality disability data in AI and algorithmic decision-making tools poses a significant risk of perpetuating and exacerbating existing barriers for people with disabilities in various aspects of life. This issue becomes more complicated when people with particular classes of disability are ignored when data is collected, as they may represent a relatively small proportion of the community. The current state will lead to performance issues with AI models in recognizing and responding to the disabled community.
Navigating The Hurdles
To overcome these technical challenges, there is a critical need to perform a risk assessment of current AI solutions for people with disabilities. This will help to identify the gap and act as a starting point for future research and development.
Strategies for Improvement
- Inclusive Datasets: More inclusive datasets can be created for testing and benchmarking AI models, and clear regulations must be established to protect the privacy of the disabled community. Additionally, synthetic data can be generated by simulation to create inclusive datasets. This approach can fill the gap by generating data by users simulating disabilities. Simulated data might not be perfect, but it is still appealing to make inclusive AI solutions.
- Design with Inclusivity: AI must be designed with inclusivity as the core and a better bias mitigation approach that ensures fairness for disabled communities. One way to mitigate this is by designing a multimodal architecture that combines several AI models for text generation, speech, and vision to accomplish more inclusive AI solutions.
- Custom AI Solutions: Designing custom AI solutions for particular user groups by feeding highly focused disability datasets can help harness AI resolution. For example, a project is working to make personalized AI models more inclusive for the over 340 million people around the world who are blind or have low vision.
- Compliance and Policies: Compliance and policies must be framed to have regulations in place to make technology accessible and responsible. The next generation must be educated to promote inclusivity in their innovations.
AI solutions offer a promising future. To make it more accessible and responsible, we must practice inclusive AI as a moral responsibility. As we move forward, our mission should be not only to build inclusive AI but to harness the power of AI to build a world that is inherently more accessible to all.
