Session
KijijiLink and DiviTrust
Doris Magiri - https://www.kijijilink.com/ Ajima Olaghere - https://divitrust.io/
Doris Magiri - https://www.kijijilink.com/ Ajima Olaghere - https://divitrust.io/
Organization's Website
Speakers
Doris Magiri - KijijiLink
Ajima Olaghere - DiviTrust
Mary Uduma
Onsite Moderator
Mary Uduma [email protected]
Rapporteur
Ajima Olaghere [email protected]
SDGs
3. Good Health and Well-Being
3.8
3.d
9.1
Targets: This talk highlights the ethical challenges of deploying AI in mental health care and proposes actionable solutions. By focusing on the importance of cultural awareness, diverse training data, and interdisciplinary collaboration, the session will emphasize how to create AI systems that prioritize user safety and inclusivity. Key Discussion Points: - AI Limitations in Addressing Suicidal Distress: Example: AI chatbots, when interacting with users in crisis, have offered inappropriate responses, underscoring their inability to understand nuanced emotional states. Call to Action: Encourage integrating human oversight and community-based interventions to complement AI tools. Cultural Awareness and Bias in AI Systems: Example: Training datasets often lack diversity, leading to culturally insensitive or irrelevant responses, particularly for marginalized populations. Call to Action: Promote the inclusion of culturally representative data and diverse perspectives in AI development. Ethical Guidelines for AI in Mental Health: Advocate for global standards, such as requiring rigorous testing in high-stakes scenarios, to prevent harm and ensure accountability. Example: Propose adopting global frameworks like UNESCO’s AI Ethics Recommendation with a focus on mental health. Data Provenance and Data Governance for AI: We lack plain language consent frameworks and protocols that empower people to exercise ownership and management of their data, the very data that is regularly ingested by AI. Consent is not only important to establish permission, but is also the beginning of respecting people’s autonomy. This includes respecting a person’s or a community’s autonomy to own and share their own journeys, like one’s journey from crisis to care. Without the inclusion of people and a pathway for people to consent and manage their data, we repeatedly abdicate care and autonomy to AI. Call to Action: Partner with and invest in advocacy groups and mission-driven companies like DiviTrust that seek to educate the public and build technology solutions to enable people own their data, manage their data, and set the terms and conditions upon which their data can be engaged and used. Personal Story and Engagement Approach: The talk will include a personal narrative: the story of an individual from a marginalized community who encountered a culturally insensitive response from an AI therapist. This anecdote will illustrate the dangers of bias and the urgency for ethical oversight. To engage both in-person and virtual audiences, the session will include interactive elements like live polls and Q&A. Key Objectives - Highlight the risks of deploying AI in mental health without human oversight, particularly in cases of suicidal distress. - Advocate for integrating cultural awareness and diverse data to mitigate bias in AI systems. - Promote community-led interventions alongside AI tools to address mental health crises. - Encourage global stakeholders to establish ethical guidelines prioritizing safety, inclusivity, and accountability. Relevance to IGF Themes This proposal addresses the subtheme of Universal Access and Digital Rights by advocating for inclusive and ethical AI development to prevent harm and ensure equitable access to safe mental health tools. It also ties into Digital Trust and Resilience by promoting transparency, safety, and collaboration in digital mental health technologies. Diversity and Inclusion The talk emphasizes the role of cultural sensitivity and diversity in AI systems to ensure equitable outcomes for all users. It advocates for the inclusion of diverse voices in AI development, from underrepresented communities to mental health professionals worldwide. Takeaway Message AI chatbots in mental health must be designed and deployed with caution. By incorporating human oversight, cultural awareness, and ethical guardrails, we can ensure that these tools complement, rather than replace, vital human connections in mental health care. Furthermore, we need to actively work toward a future where people can exercise ownership of their data, particularly for mental health and the training data AI chatbots will leverage to evolve.
3.8
3.d
9.1
Targets: This talk highlights the ethical challenges of deploying AI in mental health care and proposes actionable solutions. By focusing on the importance of cultural awareness, diverse training data, and interdisciplinary collaboration, the session will emphasize how to create AI systems that prioritize user safety and inclusivity. Key Discussion Points: - AI Limitations in Addressing Suicidal Distress: Example: AI chatbots, when interacting with users in crisis, have offered inappropriate responses, underscoring their inability to understand nuanced emotional states. Call to Action: Encourage integrating human oversight and community-based interventions to complement AI tools. Cultural Awareness and Bias in AI Systems: Example: Training datasets often lack diversity, leading to culturally insensitive or irrelevant responses, particularly for marginalized populations. Call to Action: Promote the inclusion of culturally representative data and diverse perspectives in AI development. Ethical Guidelines for AI in Mental Health: Advocate for global standards, such as requiring rigorous testing in high-stakes scenarios, to prevent harm and ensure accountability. Example: Propose adopting global frameworks like UNESCO’s AI Ethics Recommendation with a focus on mental health. Data Provenance and Data Governance for AI: We lack plain language consent frameworks and protocols that empower people to exercise ownership and management of their data, the very data that is regularly ingested by AI. Consent is not only important to establish permission, but is also the beginning of respecting people’s autonomy. This includes respecting a person’s or a community’s autonomy to own and share their own journeys, like one’s journey from crisis to care. Without the inclusion of people and a pathway for people to consent and manage their data, we repeatedly abdicate care and autonomy to AI. Call to Action: Partner with and invest in advocacy groups and mission-driven companies like DiviTrust that seek to educate the public and build technology solutions to enable people own their data, manage their data, and set the terms and conditions upon which their data can be engaged and used. Personal Story and Engagement Approach: The talk will include a personal narrative: the story of an individual from a marginalized community who encountered a culturally insensitive response from an AI therapist. This anecdote will illustrate the dangers of bias and the urgency for ethical oversight. To engage both in-person and virtual audiences, the session will include interactive elements like live polls and Q&A. Key Objectives - Highlight the risks of deploying AI in mental health without human oversight, particularly in cases of suicidal distress. - Advocate for integrating cultural awareness and diverse data to mitigate bias in AI systems. - Promote community-led interventions alongside AI tools to address mental health crises. - Encourage global stakeholders to establish ethical guidelines prioritizing safety, inclusivity, and accountability. Relevance to IGF Themes This proposal addresses the subtheme of Universal Access and Digital Rights by advocating for inclusive and ethical AI development to prevent harm and ensure equitable access to safe mental health tools. It also ties into Digital Trust and Resilience by promoting transparency, safety, and collaboration in digital mental health technologies. Diversity and Inclusion The talk emphasizes the role of cultural sensitivity and diversity in AI systems to ensure equitable outcomes for all users. It advocates for the inclusion of diverse voices in AI development, from underrepresented communities to mental health professionals worldwide. Takeaway Message AI chatbots in mental health must be designed and deployed with caution. By incorporating human oversight, cultural awareness, and ethical guardrails, we can ensure that these tools complement, rather than replace, vital human connections in mental health care. Furthermore, we need to actively work toward a future where people can exercise ownership of their data, particularly for mental health and the training data AI chatbots will leverage to evolve.
Format
This proposal is for a Lightning Talk, which is a short, focused presentation. The aim is to deliver an engaging narrative and clear call to action within the designated timeframe.
Duration (minutes)
30
Description
Session Format
This proposal is for a Lightning Talk, which is a short, focused presentation. The aim is to deliver an engaging narrative and clear call to action within the designated timeframe.
Proposal Description
Summary: AI-powered mental health tools are becoming increasingly accessible, offering scalable and affordable solutions to underserved communities. However, these systems face critical limitations in understanding human distress, particularly in high-stakes situations such as suicidal ideation. Without proper ethical guidelines or cultural sensitivity, AI mental health tools risk exacerbating harm instead of providing meaningful support.
This talk highlights the ethical challenges of deploying AI in mental health care and proposes actionable solutions. By focusing on the importance of cultural awareness, diverse training data, and interdisciplinary collaboration, the session will emphasize how to create AI systems that prioritize user safety and inclusivity.
Key Discussion Points:
AI Limitations in Addressing Suicidal Distress:
Example: AI chatbots, when interacting with users in crisis, have offered inappropriate responses, underscoring their inability to understand nuanced emotional states.
Call to Action: Encourage integrating human oversight and community-based interventions to complement AI tools.
Cultural Awareness and Bias in AI Systems:
Example: Training datasets often lack diversity, leading to culturally insensitive or irrelevant responses, particularly for marginalized populations.
Call to Action: Promote the inclusion of culturally representative data and diverse perspectives in AI development.
Ethical Guidelines for AI in Mental Health:
Advocate for global standards, such as requiring rigorous testing in high-stakes scenarios, to prevent harm and ensure accountability.
Example: Propose adopting global frameworks like UNESCO’s AI Ethics Recommendation with a focus on mental health.
Data Provenance and Data Governance for AI:
We lack plain language consent frameworks and protocols that empower people to exercise ownership and management of their data, the very data that is regularly ingested by AI. Consent is not only important to establish permission, but is also the beginning of respecting people’s autonomy. This includes respecting a person’s or a community’s autonomy to own and share their own journeys, like one’s journey from crisis to care. Without the inclusion of people and a pathway for people to consent and manage their data, we repeatedly abdicate care and autonomy to AI.
Call to Action: Partner with and invest in advocacy groups and mission-driven companies like DiviTrust that seek to educate the public and build technology solutions to enable people own their data, manage their data, and set the terms and conditions upon which their data can be engaged and used.
Personal Story and Engagement Approach: The talk will include a personal narrative: the story of an individual from a marginalized community who encountered a culturally insensitive response from an AI therapist. This anecdote will illustrate the dangers of bias and the urgency for ethical oversight. To engage both in-person and virtual audiences, the session will include interactive elements like live polls and Q&A.
Key Objectives
Highlight the risks of deploying AI in mental health without human oversight, particularly in cases of suicidal distress.
Advocate for integrating cultural awareness and diverse data to mitigate bias in AI systems.
Promote community-led interventions alongside AI tools to address mental health crises.
Encourage global stakeholders to establish ethical guidelines prioritizing safety, inclusivity, and accountability.
Relevance to IGF Themes
This proposal addresses the subtheme of Universal Access and Digital Rights by advocating for inclusive and ethical AI development to prevent harm and ensure equitable access to safe mental health tools. It also ties into Digital Trust and Resilience by promoting transparency, safety, and collaboration in digital mental health technologies.
Diversity and Inclusion
The talk emphasizes the role of cultural sensitivity and diversity in AI systems to ensure equitable outcomes for all users. It advocates for the inclusion of diverse voices in AI development, from underrepresented communities to mental health professionals worldwide.
Hybrid Participation Plan
To engage both online and in-person participants, the session will:
Utilize live polls (e.g., “Should AI tools be allowed to replace human mental health professionals?”) to spark discussion.
Takeaway Message
AI chatbots in mental health must be designed and deployed with caution. By incorporating human oversight, cultural awareness, and ethical guardrails, we can ensure that these tools complement, rather than replace, vital human connections in mental health care. Furthermore, we need to actively work toward a future where people can exercise ownership of their data, particularly for mental health and the training data AI chatbots will leverage to evolve.
Hybrid Participation Plan To engage both online and in-person participants, the session will: Utilize live polls (e.g., “Should AI tools be allowed to replace human mental health professionals?”) to spark discussion. On AI and Mental Health: 1. Do you believe AI tools are reliable enough to handle mental health crises independently? Yes No Not sure On Human Oversight: 2. Should AI mental health tools always include human oversight for high-risk scenarios (e.g., suicidal ideation)? Strongly agree Agree Neutral Disagree Strongly disagree On Cultural Sensitivity: 3. How important is cultural awareness in the design of AI mental health tools? Extremely important Somewhat important Not important On Ethical Standards: 4. Do you think global ethical guidelines for AI in mental health are currently sufficient? Yes No Unsure On Collaboration: 5. Who should be most responsible for ensuring the ethical use of AI in mental health? Policymakers Developers Community leaders Mental health professionals On Accessibility: 6. Should AI-driven mental health tools prioritize accessibility over comprehensive safety measures? Yes, accessibility first No, safety first Both equally important
Hybrid Participation Plan To engage both online and in-person participants, the session will: Utilize live polls (e.g., “Should AI tools be allowed to replace human mental health professionals?”) to spark discussion. On AI and Mental Health: 1. Do you believe AI tools are reliable enough to handle mental health crises independently? Yes No Not sure On Human Oversight: 2. Should AI mental health tools always include human oversight for high-risk scenarios (e.g., suicidal ideation)? Strongly agree Agree Neutral Disagree Strongly disagree On Cultural Sensitivity: 3. How important is cultural awareness in the design of AI mental health tools? Extremely important Somewhat important Not important On Ethical Standards: 4. Do you think global ethical guidelines for AI in mental health are currently sufficient? Yes No Unsure On Collaboration: 5. Who should be most responsible for ensuring the ethical use of AI in mental health? Policymakers Developers Community leaders Mental health professionals On Accessibility: 6. Should AI-driven mental health tools prioritize accessibility over comprehensive safety measures? Yes, accessibility first No, safety first Both equally important