Knowledge Vault 4 /72 - AI For Good 2022
Neurotech is coming: Stay tuned for an expanded AI ethics landscape
Francesca Rossi
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Concept Graph & Resume using Claude 3 Opus | Chat GPT4o | Llama 3:

graph LR classDef neurotech fill:#f9d4d4, font-weight:bold, font-size:14px classDef ai fill:#d4f9d4, font-weight:bold, font-size:14px classDef ethics fill:#d4d4f9, font-weight:bold, font-size:14px classDef applications fill:#f9f9d4, font-weight:bold, font-size:14px classDef challenges fill:#f9d4f9, font-weight:bold, font-size:14px classDef convergence fill:#d4f9f9, font-weight:bold, font-size:14px A[Neurotech is coming:
Stay tuned for
an expanded AI
ethics landscape] --> B[Neurotech: interprets/modifies
nervous system data. 1] A --> C[Classified: invasiveness, capabilities
location secondary. 2] B --> D[Existing forms: thought reading,
emotion sharing. 3] A --> E[Applications: healthcare, marketing,
defense, law. 4] A --> F[Neuroethics: ethical principles
for neurotech, neurodata. 5] A --> G[Context: key ethical
considerations. 6] G --> H[Challenges: security, accuracy,
privacy, agency. 7] B --> I[Intersects with AI/ML:
larger datasets. 8] A --> J[AI history: 60+ years,
machine learning evolution. 9] A --> K[AI in many fields:
decision making. 10] J --> L[AI limits: narrow focus,
robustness, resources. 11] A --> M[AI ethics: privacy, fairness,
transparency, audits. 12] F --> N[Neuroethics: privacy, fairness,
manipulation, security. 13] A --> O[AI ethics evolution:
awareness to practice. 14] A --> P[AIs impact on UN Goals:
careful consideration. 15] A --> Q[AI ethics lessons:
multi-stakeholder, principles, practices. 16] G --> R[Neurotech expands concerns:
data, fairness, agency, security. 17] G --> S[AI, neuroscience: different data
ethics histories. 18] G --> T[Experts: prepare for AI/
neurotech convergence. 19] A --> U[Key takeaways: neurotechs coming,
needs collaboration. 20] class B,C,D,I neurotech class J,K,L ai class F,M,N,O,P ethics class E applications class H,Q,R,S,T,U challenges

Resume:

1.- Neurotechnology (neurotech) collects, interprets, infers, or modifies nervous system data. It's an emerging technology with potential to transform many aspects of life.

2.- Neurotech is classified by invasiveness (implanted vs wearable) and capabilities (sensing, modulating, or both). Location of interface is less important.

3.- Neurotechnologies that exist in some form today: reading thoughts/actions, visualizing imagery, changing sensations/perceptions, sharing emotions/memories. Maturity and accuracy vary.

4.- Neurotech has applications in healthcare, marketing, entertainment, defense, law. Examples: treating illness, monitoring fatigue/attention, restoring sensation, enabling mind-typing.

5.- Neuroethics studies ethical principles and implications related to neurotech, neurodata, and neuroscience. It involves applying ethics to change practices and policies.

6.- Context of neurotech creation and use significantly impacts ethical considerations. Key questions: what data, intended use, who, how, for whom.

7.- Some neurotech challenges are common to other technologies (security, accuracy, sustainability). Others are heightened due to complexity/sensitivity (privacy, agency, identity).

8.- Neurotech rarely operates alone - it requires and intersects with AI/ML, especially as datasets get larger, more complex, and collected outside labs.

9.- AI started 60+ years ago with humans coding intelligent problem-solving. 1980s introduced data-driven machine learning. Computing power enables current AI success.

10.- AI is found in digital assistants, transportation, customer service, media, healthcare, finance, jobs, law. It's used in high-stakes decision making.

11.- AI limitations include narrow specialization, lack of robustness/adaptability to tweaked inputs, and high computing resource needs which creates power imbalances.

12.- AI ethics issues include data privacy, fairness, discrimination, transparency, accountability, social impact, human agency, explainability. External audits and regulations address this.

13.- Neuroethics issues include privacy, fairness, access, profiling, manipulation, societal impact, human autonomy/agency, identity, accuracy, security, well-being.

14.- AI ethics has evolved through phases of awareness, published principles, and now practice with regulations, standards, corporate practices, and education.

15.- AI can advance or detract from the UN Sustainable Development Goals. Impacts must be carefully considered.

16.- AI ethics lessons: multi-stakeholder approach, principles aren't sufficient, company-wide practices, technical + non-technical solutions, mistakes are part of the process.

17.- Neurotech introduces expanded concerns around data, explainability, accountability, fairness, access, profiling, manipulation, privacy, autonomy, agency, identity, accuracy, security, well-being.

18.- AI and neuroscience communities have different histories with human/animal data ethics. Collaboration is needed to address converging neurotech and AI ethics issues.

19.- AI and neuroscience experts need to prepare for the convergence of AI and neurotech by updating frameworks, engaging stakeholders, and considering affected communities.

20.- Key takeaways: neurotech is coming, it rarely operates without AI, and this introduces expanded ethical issues that require multi-stakeholder collaboration to address.

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