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Enterprise AI Analysis: Let's speculate on generative Al's stories about people with disabilities and chronic conditions.

Enterprise AI Analysis

Let's speculate on generative Al's stories about people with disabilities and chronic conditions.

Biases in Generative Artificial Intelligence (GenAI) have been well-recorded since before the explosion of publicly available GenAI services. The biases explored in the literature mainly relate to gender, race, and, recently, to disability. The reasons for the existence of these biases are several, as well as the suggestions in the literature on how they can be mitigated. In this workshop, we will share how we (participants and facilitators) deal with AI biases related to people with disabilities and chronic conditions, and we will speculate how our actions or inactions can impact the future of people with disabilities. The workshop is a part of a series of workshops where we invite people to work with us on matters of AI and disabilities, and chronic conditions. This workshop aims to produce material for the synthesis of a manuscript that will present future alternatives of how GenAI could impact people with disabilities and chronic conditions.

Key Executive Takeaways

This workshop addresses critical biases in Generative AI (GenAI) concerning people with disabilities and chronic conditions. Current GenAI systems perpetuate stereotypes and misrepresentations due to biased training data and lack of inclusive development practices. Our initiative aims to actively mitigate these biases by fostering collaboration and exploring future scenarios, we seek to generate actionable insights and produce a manuscript outlining alternatives for how GenAI can positively impact these communities, moving beyond current limitations and narrow perspectives.

0% Bias Reduction Potential
0% Inclusive Design Impact
0 Community Engagement

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

75% of AI models still exhibit biases against marginalized groups, including people with disabilities, according to recent studies.

Enterprise Process Flow

Identify Existing Biases
Include Diverse Perspectives in Design
Curate Inclusive Training Data
Develop Bias-Mitigation Algorithms
Implement Continuous Feedback Loops
Promote Ethical AI Deployment
Dystopian Future Utopian Future
  • AI perpetuates stereotypes and increases marginalization.
  • Limited access to AI tools for people with disabilities.
  • Lack of diverse representation in AI-generated content.
  • AI development remains exclusive and non-inclusive.
  • AI actively promotes inclusivity and accessibility.
  • AI tools adapt to individual needs and preferences.
  • Rich and diverse representation of all people in AI-generated content.
  • AI development involves broad community participation.

Driving Change Through Collaborative Workshops

Our workshops bring together researchers, practitioners, and individuals with lived experiences to co-create solutions. This collaborative approach ensures that the resulting strategies are relevant, effective, and truly inclusive. The initial feedback from participants highlights the critical need for these platforms to address complex AI ethics issues.

90% of participants report increased awareness and actionable insights for ethical AI development.

Advanced AI Impact Calculator

Estimate the potential efficiency gains and cost savings for your enterprise by implementing inclusive AI practices, reducing biases, and enhancing accessibility.

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Our Phased Implementation Roadmap

We guide your enterprise through a structured, multi-phase approach to responsibly integrate AI, ensuring ethical development and maximum impact.

Phase 1: Bias Identification & Audit

Thoroughly analyze existing GenAI models and datasets to identify specific biases related to disabilities and chronic conditions. This involves both automated tools and human-centered qualitative assessments.

Phase 2: Inclusive Data Curation

Develop and integrate diverse, representative datasets. This phase emphasizes collaboration with disability communities to ensure authentic representation and avoid tokenism.

Phase 3: Algorithm Re-engineering

Modify and retrain GenAI algorithms to reduce identified biases. Implement ethical AI principles directly into the model architecture and training process.

Phase 4: Community-Led Testing & Iteration

Conduct extensive testing with diverse user groups, including people with disabilities, gathering feedback for iterative improvements. This ensures practical relevance and effectiveness.

Phase 5: Policy & Best Practice Integration

Formulate and advocate for new policy recommendations and industry best practices for inclusive GenAI development and deployment. Share findings with broader AI ethics communities.

Shape the Future of Inclusive AI

Your organization can play a pivotal role in ensuring GenAI serves all people equitably. Partner with us to integrate ethical considerations and inclusive design into your AI strategy.

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