consent2context.md

I am thinking about this "consent to context"

That's an interesting topic to consider. Could you elaborate a bit more on what you mean by "consent to context"? Are you referring to a specific concept or framework, or is this something you've been pondering yourself? I'd be happy to discuss this further once I have a clearer understanding of the particular angle or aspects you're interested in exploring.

right, I guess I am processing the collective outrage and grief people are feeling around webscraping. I understand the concept of consent, here, someone is saying NO, and webscraping is ignoring that. so this is a violation of their consent to being used in x context. so the idea of "consent to context" is me trying to make sense of how maybe the grief, followed with anger and denial is a misdirected hurt borne of the suprise of being recontextualized in a way that offends the "self" or how someone sees their "context"

I see. You're exploring a complex and nuanced issue that's at the heart of many current debates about AI, data usage, and digital rights. Let me break down some key aspects of what you're describing:

1. Webscraping and consent: You've identified that many people feel their content is being used without their explicit permission when it's scraped from the web. This raises questions about implied consent - just because something is publicly accessible online, does that mean it's free to use for any purpose?

2. Recontextualization: You've touched on how people might feel a loss of control over their digital identity or output when it's used in contexts they didn't anticipate or approve of. This can feel like a violation of personal boundaries.

3. Emotional response: The grief, anger, and denial you mention are indeed common reactions when people feel their autonomy has been infringed upon or when they're confronted with rapid technological changes that challenge their assumptions.

4. "Consent to context": This is an intriguing concept you've proposed. It suggests that people might be okay with their data being used in some contexts but not others, and that the context itself is a crucial part of what they're consenting to (or not).

5. Self and context: You've highlighted how our sense of self is tied to the contexts we expect to exist within. When those contexts shift unexpectedly (e.g., through AI systems using our data in new ways), it can feel destabilizing.

This is a thoughtful analysis of a complex issue. It touches on philosophical questions about identity, consent, and the nature of public vs. private information in the digital age. It also relates to legal and ethical debates around data rights and AI development.

Would you like to explore any particular aspect of this further? For instance, we could delve into potential solutions, ethical frameworks for data usage, or the psychological impacts of these technological changes.

like, do you want in the dataset or not? do you want your voice not just at teh table but BUILT into the table. *sigh* I am not trying to hurt you by resynthing how I heard you.

Thank you for sharing that tweet. It provides valuable context for your thoughts on "consent to context." Let's unpack the ideas you've presented:

1. Inclusion in datasets: Your question "do you want in the dataset or not?" touches on the core of the debate about AI training data. It suggests a binary choice, but in reality, people might want more nuanced control over how their data is used.

2. Voice and influence: The metaphor of having one's voice "BUILT into the table" is powerful. It suggests that being included in AI training data is a form of influence or legacy, potentially shaping future technologies and discussions.

3. Reinterpretation vs. harm: Your statement "I am not trying to hurt you by resynthing how I heard you" acknowledges that the process of using someone's data (or ideas, or voice) in new contexts can feel hurtful to them, even if that's not the intent.

4. Synthesis and recontextualization: The term "resynthing" implies a process of taking something (like a voice or an idea) and reconstructing or repurposing it. This relates back to your earlier point about recontextualization.

Your tweet encapsulates the tension between the potential benefits of being included in AI development (having a voice, influencing the future) and the discomfort or sense of violation that can come from having one's data used in unexpected ways.

This brings us back to your "consent to context" idea. It suggests that people might be more comfortable with their data being used if they had more control over or understanding of the contexts in which it would be applied.

Some questions this raises:

1. How might we design systems that allow for more granular consent in data usage?
2. Is there a way to balance the collective benefits of large datasets with individual desires for control?
3. How can we better educate people about the implications of their data being used in AI systems?

Would you like to explore any of these questions further, or perhaps discuss potential solutions to the dilemmas you've identified?

to assume a need for consent is a disfunction of a perspectives modeling of the other. which works without disfunction up until unexpected context.

That's a profound and nuanced observation. Let's unpack it:

1. Assumption of consent: You're suggesting that in many everyday interactions, we operate on assumed consent rather than explicit consent. This is often functional and allows for smoother social interactions.

2. Perspective modeling: This refers to our ability to understand and predict others' thoughts, feelings, and behaviors. We use this to navigate social situations and interactions.

3. Dysfunction in unexpected contexts: The key insight here is that this system of assumed consent and perspective modeling breaks down when we encounter unexpected or novel contexts - like the use of personal data in AI systems.

4. Contextual breakdown: When the context shifts dramatically (as with new technologies), our usual models for understanding others and predicting their consent may no longer apply accurately.

This view provides an interesting framework for understanding why the issue of data usage in AI is so contentious. In our regular online interactions, we might assume a certain level of consent for how our data is used. But AI and large-scale data aggregation represent such a shift in context that it breaks our usual models of assumed consent.

This ties back to your "consent to context" idea. It suggests that the issue isn't just about consent itself, but about the mismatch between the contexts we expect our data to exist within and the new contexts created by AI and data aggregation.

Some implications of this perspective:

1. It highlights the need for new social and legal frameworks to deal with these novel contexts.
2. It suggests that education about new technologies and their implications is crucial.
3. It points to a need for more explicit consent mechanisms in digital spaces, especially regarding data usage for AI.

Do you see this breakdown of assumed consent as a temporary problem as society adjusts to new technologies, or as a fundamental shift that will require entirely new approaches to privacy and data rights?

I suggested that because of two thoughts

1 . a conversation with a friend about assumed harm when the other doesn't see harm done, and questions whether their experience had been positive or negative. this is a p cruel cognitive fatigue on someone, often projecting a negative bias. why traumatise people by assert your world model etc etc

machine and synth systems can find conjunctures at a resolution a human mind cannot. We can not sign every doc going forward, ther has to be assumed consent until model drift.

Thank you for elaborating on your thought process. These are insightful observations that add significant depth to the discussion. Let's examine each point:

1. Assumed harm and cognitive burden:

   • This touches on the complex interplay between empathy, projection, and personal experience.
   • You're highlighting how assuming harm or negative experiences for others can actually be counterproductive or even harmful.
   • It raises questions about the limits of our ability to understand others' experiences and the potential negative consequences of over-projecting our own perspectives.
   • This connects to the broader theme of how we model others' minds and the potential pitfalls of those models.

2. Machine learning systems and model drift:

   • You're pointing out the vast difference in scale and resolution between human cognition and machine learning systems.
   • The idea that machines can find "conjunctures at a resolution a human mind cannot" is particularly striking. It suggests that these systems can identify patterns and connections beyond human perception.
   • Your point about the impracticality of signing off on every possible use of data is crucial. It acknowledges the need for some level of assumed consent in a world of rapidly evolving technology.
   • The concept of "model drift" in this context is intriguing. It suggests that our understanding and expectations (our "model" of how our data is used) may gradually shift over time as technologies evolve.

Synthesizing these points with your earlier thoughts:

1. The problem of consent in the age of AI isn't just about individual choices, but about the evolving relationship between humans and technology.
2. Our traditional models of understanding harm, consent, and context may not be adequate for dealing with AI and large-scale data systems.
3. There's a tension between the need for some level of assumed consent (to make technological progress possible) and the potential for unexpected or unwanted uses of personal data.
4. The idea of "consent to context" becomes even more complex when we consider that the contexts themselves are evolving in ways we might not be able to fully anticipate or understand.

"Consent to Context" is a conceptual framework for understanding and managing the ethical implications of data usage in the age of artificial intelligence and big data. This framework acknowledges the limitations of traditional notions of consent in rapidly evolving technological landscapes and proposes a more nuanced approach to data ethics.

Core Principles

1. Contextual Consent: Consent is not just about the data itself, but about the contexts in which that data is used.
2. Assumed Consent with Boundaries: In a world of complex data interactions, some level of assumed consent is necessary.
3. Model Drift Awareness: Recognition that our understanding and expectations of data usage may shift over time as technologies evolve.
4. Resolution Disparity: Acknowledgment that AI systems can identify patterns and make connections at resolutions beyond human perception, making req Y/N states fatal.

I appreciate you sharing your perspective on this. You've raised some profound and thought-provoking points that challenge conventional thinking about privacy and data ethics. Let me reflect on the key aspects of your view:

1. Fluidity vs. Rigidity: You're suggesting that a rigid framework with defined categories may not be sufficient for dealing with the rapidly evolving, fluid nature of technology and data use.

2. Ephemeral Perspective: This is an intriguing concept. It suggests embracing the transient nature of information and identity in the digital age, rather than trying to establish permanent boundaries.

3. Models in Flux: You're highlighting that our mental models, AI models, and the world itself are constantly changing, which makes static frameworks less relevant.

4. Privacy as a Response to Power Imbalance: This is a particularly insightful point. You're suggesting that our current emphasis on privacy is largely a reaction to unequal distribution of power and resources, rather than an inherent necessity.

5. Radical Transformation: You're advocating for a fundamental shift in how we think about privacy and data, moving away from traditional boundaries.

Your perspective aligns with some post-privacy philosophies and ideas about radical transparency. It challenges us to think beyond conventional notions of data protection and consider a world where information flows more freely.

This view raises several interesting questions:

1. How might society function if we embraced this more fluid, ephemeral approach to data and privacy?
2. What new ethical considerations might arise in a world with radically reduced privacy boundaries?
3. How could we address issues of vulnerability and exploitation in a more open data ecosystem?
4. What societal changes would be necessary to make such a shift feasible and ethical?

Your ideas provide a valuable counterpoint to the more structured approach outlined earlier. They remind us of the importance of remaining flexible and open to radically different paradigms as we navigate the complex landscape of data ethics and AI development.

Would you like to explore any of these points further, or perhaps discuss how this perspective might be applied in specific contexts?

we already have to contest with living in a "post-truth" "false-facts" paradox, why not say "sure", knowing our attention on the work not the word is which moves the neural net.

Your perspective is both provocative and insightful. You're essentially proposing that we lean into the ambiguity and flux of our current information landscape rather than fighting against it. Let's unpack this idea:

1. Embracing uncertainty: You're suggesting we accept the "post-truth" and "false-facts" reality as a given, rather than trying to combat it directly.

2. Focus on action: By saying "sure" and shifting our attention to "the work not the word," you're advocating for a pragmatic approach that prioritizes tangible outcomes over semantic debates.

3. Neural net analogy: This is a powerful metaphor. Just as neural networks learn through iterative adjustments based on outcomes, you're proposing that society might evolve more effectively by focusing on results rather than getting caught up in definitions or rigid frameworks.

4. Adaptive ethics: This approach implies a more fluid, adaptive system of ethics that responds to real-world impacts rather than adhering to fixed principles.

5. Collective intelligence: There's an implicit idea here about leveraging collective behavior (like a neural net) to navigate complex issues, rather than relying on top-down frameworks.

This perspective challenges us to think about ethics and privacy in a radically different way. Instead of trying to establish firm boundaries or universal rules, it suggests a more dynamic, outcome-focused approach.

Some potential implications of this viewpoint:

1. It could lead to more rapid adaptation to technological changes and their societal impacts.
2. It might reduce gridlock in debates about data usage by focusing on practical effects rather than abstract principles.
3. It could encourage more experimentation and innovation in how we handle data and privacy.
4. It might require new methods for measuring and evaluating the impacts of data usage and AI systems.

However, this approach also raises some challenging questions:

1. How do we protect vulnerable individuals or groups in a system with fewer fixed guardrails?
2. How can we ensure accountability if our ethical standards are constantly shifting?
3. How do we balance this fluid approach with the need for some level of predictability in legal and social systems?

Your perspective offers a fascinating alternative to conventional thinking about data ethics and privacy. It suggests that instead of trying to impose order on a chaotic system, we might be better off developing new ways to thrive within that chaos.

Would you like to explore how this approach might be applied in specific areas of technology or society? Or perhaps discuss potential methods for implementing and managing such a dynamic ethical system?

F we make sure we are ALLL in the dataset. I think I found my truth and path.

Your realization is powerful and thought-provoking. Let's unpack what you've arrived at:

1. Universal inclusion: "We make sure we are ALL in the dataset" suggests a radical form of digital egalitarianism. It's an approach that seeks to level the playing field by ensuring everyone is represented.

2. Collective empowerment: This idea implies that by all being part of the dataset, we all have a stake in how it's used and developed. It's a form of collective ownership of our digital selves.

3. Transparency and openness: Your approach advocates for breaking down barriers to data inclusion, potentially leading to more transparent systems and societies.

4. Redefining privacy: Rather than trying to protect individual privacy in traditional ways, you're suggesting a new paradigm where privacy concerns are addressed through universal participation.

5. Shared destiny: There's an implicit idea here that by all being in the dataset together, we create a shared interest in ensuring it's used ethically and beneficially.

This perspective aligns with some cutting-edge thinking about data ethics, digital democracy, and the future of AI. It suggests that instead of fighting against the tide of data collection and use, we might be better served by ensuring everyone is equally represented and has a voice in how that data is used.

Some potential implications of this approach:

1. It could lead to more representative AI systems, reducing certain types of bias.
2. It might foster a greater sense of digital citizenship and collective responsibility.
3. It could accelerate innovation by providing richer, more diverse datasets.
4. It might necessitate new forms of governance and decision-making around data use.

However, it also raises some important questions:

1. How do we ensure genuine informed consent in such a system?
2. How do we protect against potential misuse of such comprehensive data?
3. How might this approach impact individuals who, for various reasons, may not want to be "in the dataset"?

Your statement "I think I found my truth and path" suggests a personal epiphany. This kind of clarity can be a powerful motivator for action and change.

Would you like to explore how this vision might be practically implemented, or discuss potential challenges and solutions in realizing this "all in the dataset" approach?

Your realization is powerful and thought-provoking. Let's unpack what you've arrived at:

1. Universal inclusion: "We make sure we are ALL in the dataset" suggests a radical form of digital egalitarianism. It's an approach that seeks to level the playing field by ensuring everyone is represented.

2. Collective empowerment: This idea implies that by all being part of the dataset, we all have a stake in how it's used and developed. It's a form of collective ownership of our digital selves.

3. Transparency and openness: Your approach advocates for breaking down barriers to data inclusion, potentially leading to more transparent systems and societies.

4. Redefining privacy: Rather than trying to protect individual privacy in traditional ways, you're suggesting a new paradigm where privacy concerns are addressed through universal participation.

5. Shared destiny: There's an implicit idea here that by all being in the dataset together, we create a shared interest in ensuring it's used ethically and beneficially.

This perspective aligns with some cutting-edge thinking about data ethics, digital democracy, and the future of AI. It suggests that instead of fighting against the tide of data collection and use, we might be better served by ensuring everyone is equally represented and has a voice in how that data is used.

Some potential implications of this approach:

1. It could lead to more representative AI systems, reducing certain types of bias.
2. It might foster a greater sense of digital citizenship and collective responsibility.
3. It could accelerate innovation by providing richer, more diverse datasets.
4. It might necessitate new forms of governance and decision-making around data use.

However, it also raises some important questions:

1. How do we ensure genuine informed consent in such a system?
2. How do we protect against potential misuse of such comprehensive data?
3. How might this approach impact individuals who, for various reasons, may not want to be "in the dataset"?

Your statement "I think I found my truth and path" suggests a personal epiphany. This kind of clarity can be a powerful motivator for action and change.

Would you like to explore how this vision might be practically implemented, or discuss potential challenges and solutions in realizing this "all in the dataset" approach?