23 hours Ago By Simone Lobo
Explainer Briefly Slides At PrivacyNama 2024, speakers discussed the way to best develop AI while ensuring privacy for users. The law often mandates AI model developers create models in a way that protects the rights of users. However, approaches to this may differ across the spectrum.
Some organisations may choose to adopt a harms-based approach, wherein they remedy any deficiencies based on feedback. Conversely, speakers at PrivacyNama suggested ‘Privacy by Design’ as a more efficient and fairer alternative. Data Governance begins with the design The speakers said that to ensure compliance with privacy regulations, developers must consider data protection principles right at the inception of the model.
Udbhav Tiwari, Head of Global Product Policy, Mozilla Foundation, said that ‘Privacy by Design’ is integral to data protection principles, “there’s almost no point trying to make an AI system comply with privacy regulation unless you’re accounting for those characteristics and principles, right from the initial design of the product.” He said there are two ways to ensure that an AI model protects the privacy of individuals. First, the developers can train models on data sets that follow certain privacy considerations and do not include information that can violate privacy.
Conversely, developers can code a model ,explicitly, not to generate certain outputs. He said that without this, “you will almost certainly end up causing or creating risks for your product that will end up making it much harder for it to be able to both comply with regulation, as well as not be subject to the adversarial environments.” “The scale of AI is way wider than any of the enforcement capacities available, and therefore, we have to think of safeguards at the stage of training data at the stage or even before training data,” said Beni Chugh, Head – Future of Finance, Dvara Research.
Srinidhi Srinivas, Partner, Ikigai Law, noted that the applicable and relevant data protection principles may differ at stage. For instance, during the development stage, the principles of exception of publicly available data may apply, and at the deployment stage, the principles of consent seeking and consent withdrawal may apply. Can developers automate regulations? As speakers discussed the need for regulating AI, another idea proposed was to create self-policing and self-monitoring AI systems that could flag if they detected any inconsistency in following the law, employing AI to fill in the gap of regulation.
Tiwari said this would be quite challenging as “all code is controlled by human beings”. He said, “Technology can certainly help you comply with legal regulations, and sometimes it’s necessary to comply with legal regulations. But I think extending that to say that technology can help you monitor the harms that might occur prior to the product being deployed or even after being deployed is a much harder question.
” He also warned that AI systems have a tendency to hallucinate. Further, he warned that models that are that advanced and powerful may also pose a safety risk. Instead he said, “I think [technology] can play a role of creating the records that are necessary to investigate whether legal compliance was done or not .
..But I think to catch violations that are happening outside in the world because of bad privacy practices is a really, really hard thing to do.
” He also noted that there were many startups and organizations have products can record internal practices and set up internal, frameworks for documentation of data. He said that tools to flag non-compliance would be a lot more effective if they were widely available and attested by regulators. He added that these data governance tools can ensure that developers are meeting their obligations.
The tools can also help with investigation as companies will no longer be able to shirk away responsibility and ensure their compliance. Pundarikaksh Sharma, the session chair summed up, that technological progress in this sector was aimed at increasing accountability through transparency, logging the steps to a model’s output and the manners in which the input were provided and the output itself. Should there be exemptions to any forms of data? The Digital Personal Data Protection (DPDP) Act says that data protection principles do not apply to personal data that is made or caused to be made publicly available by the user to whom such personal data relates.
Sreenidhi Srinivas said that an exemption for any publicly available data is “too wide an exemption” and that “just because some information is out there doesn’t mean it’s out there for us to pick up”. She said that one must consider the provenance of that data, the source and if the individual made the data publicly available themselves. Beni Chugh said she was against exemption for certain forms of data and could not think of a use case where exemption could apply.
“Privacy is really not confidentiality. If someone is accessing my data and using it in a bounded manner that is agreeable and lawful, my privacy is intact, and I don’t see why any particular actor or agent in the society would need exemptions over and beyond,” she said. Tiwari agreed saying that “blanket exemption” may not be fair because the same product can be used for multiple different use cases and themes.
Is Strict liability the way to go? Tiwari said that strict liability could apply to certain use cases where there is a real tangible threat to life” or ones that “would lead to outcomes that are so discriminatory that they’re considered universal norms that should not be violated.” Srinivas noted that India’s DPDP Act had restriction on tracking, monitoring targeted advertising based on children’s data, explaining that regulators decide the terms of strict liability based in the vulnerability of the community. “Strict liability largely means that you just really need to be extra careful about what you do and that the consequences of what would happen if things go wrong will be more severe than if things would just normally go wrong.
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. It’s about like, ‘What if a person doesn’t get healthcare and dies because of clearly documented harm that occurred because of the system?’ So the threshold to require it and prove it is also quite high and therefore the consequences are also quite high,” Tiwari said. Chugh said that under strict liability, developers must fine-tune other ex-ante regulations such as heightened transparency, heightened accountability, heightened risk assessment, to the risk level.
However, she questioned if the regulators at the government level, had the capacity to implement solutions. She asked whether the industry has the confidence to really open up to the government and regulators? “Can the regulator actually pick apart and still guarantee that their trade secrets are fine or their IP’s are fine?,” she opined. Data erasure requests Speakers also discussed if there must be obligations wherein AI developers inform all participants when they process their data.
Srinivas said this provision may cause complications, as it is unclear if the onus must be on the developer or the deployer. “There is a base model on top of which, other deployers may have built their service or their end product, and in these scenarios who’s really calling the shots with respect to their data?”, she questioned. “And also there may be base training data in the foundational model and there may be certain training data that the deployer has added on top of that layer.
Would that mean the deployer is responsible for that entire training data set?”, she added. Udbhav Tiwari said, “I don’t think the technology is the problem for how many people you can send data requests to and whether they have to comply with it. But I don’t see a world in which one company merely telling the other company that they got an erasure request, would automatically make the second company erase the data.
That is an obligation that needs to come from either the law or the regulator, not from one company to another.” Also Read:.
