Privacy and AI #14
In this edition of Privacy and AI:
PRIVACY
• Privacy and AI for AI Governance Professional (AIGP) certification
• Engineering DSRs into GenAI models
• AI & Personal Data by the DSK
• 2023-24 Survey of Canadian businesses on privacy-related issues
• Venezia's Smart Control Room
ARTIFICIAL INTELLIGENCE
• Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (Draft)
• Safety Recommendations for GenAI systems
• Understanding ISO 42001 (AI Management System)
• GPT-4o safety evaluations
• 10 AI terms that everyone should know
• AI Risk Atlas (IBM)
• 101 real-world gen AI use cases featured at Google Cloud Next ’24
EVENTS
• Deep dive into the privacy minefield of Gen AI - RISK AI Digital
PRIVACY
Privacy and AI for AI Governance Professional (AIGP) certification
Last week I received some inquiries concerning whether “Privacy and AI” is useful for the Certified AI Governance Professional (AIGP) exam.
I’ve never thought about it since I haven't taken the exam.
I’ve started writing it long before AIGP was conceived and it took me nearly four years to complete it (considering that it is part of my PhD thesis).
Comparing AIGP BoK and the contents of Privacy and AI, I can say that it covers (totally or partially) most of the domains.
- Foundations of AI, types of algorithms and how they work, with a focus on, but not limited to, machine learning techniques.
- AI impacts on fundamental rights, with particular focus on transparency and fairness
- Applicability of laws to AI systems. This has a preponderant focus on how data protection laws apply to AI, but also the AI Act (at that moment still draft) applies. There is also a handy checklist with the requirements of the AI Act (EU Commission version)
- Understanding how the different stages of the AI lifecycle produce different types of risks
- Critically, implementing measures to mitigate the impacts produced by AI systems.
So overall, I think that Privacy and AI can help expand the understanding of this area and support AIGP candidates, with a language that most privacy professionals are familiar with. More broadly, it will assist in the day-to-day work governing AI.
However, it is important to note that this is NOT OFFICIAL training material, nor was approved by IAPP in any way. This could only serve as complementary material to the official provided by the IAPP.
Engineering DSRs into GenAI models
ICO launched the 4th call for evidence on GenAI
Personal data may be included in:
- the training data;
- data used for fine-tuning, including data from RLHF and benchmarking data;
- the GenAI outputs
- user queries (eg when a DS includes PD in a prompt).
ICO Considerations
1. Development
1.1 The right to be informed
- Some cases regard direct PD collection (eg prompts)
- PD collected from 3P: typical data collection (web-scraping)
- On Art 14 disproportionate effort exemption: processing PD to develop GenAI models “is likely to be beyond people’s reasonable expectations at the time they provided data to a website”
GenAI providers must:
- publish specific, accessible information on the sources, types and categories of PD used to develop the model.
- publishing specific, accessible explanations of the purposes for which PD is being processed and the lawful basis for the processing
- provide prominent, accessible mechanisms for individuals to exercise their rights
ICO does not rule out the application of PETs to reduce the identifiability of the data
1.2 The right of access
- ICO expects GenAI developers to have methods to facilitate and respond to access requests (regardless of whether the DSR relates to training, fine-tuning or deployment)
- Developers must explain why and demonstrate that they cannot identify individuals (in the training data or anywhere else), if they invoke they cannot honour the request
1.3 Erasure and objection
- Fulfilling these rights may be difficult due to memorisation issues (LLMs retain information about the data used in training) and they can unintentionally output sections of the training data they have ‘memorised’ without being explicitly asked.
- The use of filters may help mitigate these risks. Input filters amend specific user prompts and output filters block specific model outputs [NB: regarding output filters, the DSK considered them not enough]
- ICO also warns about the implications for the fairness and statistical accuracy of the model itself (in particular regarding specific groups of people)
2. Deployment
- ICO stressed that DSRs should be respected across the AI lifecycle and supply chain, including during deployment (eg. data inputted into the live model after launch or any outputs that can constitute PD).
Link here
AI & Personal Data by the DSK
The German Data Protection authorities issued guidance about the selection, implementation and use of AI systems.
Summary below
1) Use and selection of AI systems
• Define the scope of use (AI systems are generally develop for certain specific uses)
• Check whether the use of AI is lawful in the particular context (eg. emotion recognition systems in the workplace are forbidden by the AIA)
• When business says no PD is not processed, ensure this is the case
• While the guidance acknowledges that controllers have no control over the training data (and the lawfulness of its processing), they must ensure that errors in the training data do not affect the expected outcomes
• if unsure about the legal basis, check the Baden-Wuttemberg guidance
• on ADM, ensure that human involvement is not a token gesture
• Closed (in-prem) systems allow more control for users since data is not further used by third parties and is the preferred option. Open systems (cloud-based) may enable the further use of input data (eg training, sharing), potentially sharing data across borders. Consider potential data breaches, provision on cross-border transfers and confidentiality levels of the data (personal or not).
• controllers should request enough information from the provider. This includes information about the “logic involved” which is an “explanation of the method of data processing … in relation to the functioning of the program sequence in connection with the specific application”
• check whether the input data is used for training. If it is not possible, a separate legal basis may be needed
• check whether users can disable input history
• ensure DS can exercise their rights. Particular issues arise regarding: a) accuracy: users should be allowed to rectify inaccuracies, for example by “correcting data or through retraining/fine tuning”. b) erasure: it should be “permanently impossible” to restore the personal reference, so the suppression of unwanted output by downstream filters does not constitute erasure (data is still available in the model).
• involve DPO and workers' representatives as needed
2) Implementation of AI systems
• If using a cloud provider, sign a DPA (generally C-P). JC sometimes may arise especially in case of cooperation between organizations (AI system is trained with different dataset or if AI system is further developed into new AI systems by other orgs “on the platform of one entity”.
• set appropriate internal guidance on how to use the systems (AI governance)
• conduct DPIAs
• employees should not use personal accounts (set company accounts for employees)
• Implement PbD measures
• implement security measures
• Train employees
• Stay updated
3) Use of AI systems
• Avoid inputting personal data, and be careful of the inferences AI systems can make from non-personal data
• extreme caution when using sensitive data
• check results for inaccuracies and discrimination
Link here
2023-24 Survey of Canadian businesses on privacy-related issues
The Office of the Privacy Commissioner of Canada (OPC) commissioned a private company to conduct quantitative research with Canadian businesses on privacy-related issues.
AI-related aspects of the survey
• Limited use of AI for business operations
6% of business representatives surveyed reported that their company uses AI for business operations, but the vast majority (93%) do not.
• Top uses of AI
- improve business operations
- improve efficiency and to make decisions
• One-quarter of companies not using AI for business operations are somewhat or very likely to do so in the next 5 years
General privacy insights
• Most CA companies are aware of their responsibilities under CA’s privacy laws and have taken steps to ensure they comply with these laws.
88% of the companies are at least moderately aware of their privacy-related responsibilities and 76% have taken steps to ensure they comply with CA laws
• +65% of CA businesses have implemented the following privacy practices:
- designated a privacy officer (56%);
- put in place procedures for dealing with customer complaints about the handling of personal information (53%)
- or responding to customer requests for access to their personal information (50%);
- developed internal policies for staff that address privacy obligations (50%). 33% regularly provide staff with privacy training and education.
• Many companies have a privacy policy (notice) in place, but over time, fewer companies report having one. Most companies that have a privacy policy use plain language to explain their practices with respect customers’ personal information.
• Few companies have experienced a data breach, but half are prepared to respond to a breach involving personal information.
Link here
Venezia's Smart Control Room
Some time ago Venezia inaugurated the Smart Control Room (SCR) project, a hub for the management of mobility and urban services.
The main features made available by the SCR platform concern:
- real-time geolocation of individuals
- origins of commuters/visitors,
- traffic control and local public transport,
- monitoring pedestrian flows,
- prediction of visitors and flows,
- sentiment analysis,
- assisted navigation of city visitors
The information is collected from many sources, but in particular from the mobile operator that provides aggregated information to the city.
In the images, you can see that this information is displayed in virtual squares (150m*150m) and it indicates:
- the number of individuals (906)
- foreigners (269)
- Italians (637)
-- Venetian residents (123)
-- commuters (135)
-- visitors from the Veneto Region (256)
-- visitors from other Italian regions (123)
Excellent documentary (in Italian) to understand how it works and concerns (in comments)
Link here
ARTIFICIAL INTELLIGENCE
Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (Draft)
The NIST produced a companion resources for GenAI to the AI RMF, which serves both as a use-case and a cross-sectoral profile of the AI RMF.
This profile defines a group of risks that are novel to or exacerbated by the use of GenAI. The risks identified are:
1. Eased access to CBRN Information
2. Confabulation (hallucinations)
3. Eased production of Dangerous or Violent Recommendations
4. Data Privacy
5. Environmental (carbon footprint)
6. Human-AI Configuration (algorithmic aversion, automation bias, etc)
7. Information Integrity
8. Information Security
9. Intellectual Property
10. Obscene, Degrading, and/or Abusive Content
11. Toxicity, Bias, and Homogenization
12. Value Chain and Component Integration
Crucially, it provide specific actions to manage GenAI risks, organized by AI RMF sub-categories (GOVERN, MAP, MEASURE, MANAGE)
Recommended by LinkedIn
Any organization deploying GenAI could profit from this resource, in particular those that have implemented or are implementing NIST AI RMF.
Important is that not all actions apply to all actors (eg, some relate to developers and may not be relevant for deployers), but some are definitively critical for every company deploying GenAI tools, examples:
GOVERN
1.1 - Disclose use of GAI to end users.
1.2 - Define acceptable use policies for GAI systems deployed by, used by, and used within the organization
1.5 - Develop or review existing policies for authorization of third party plug-ins and verify that related procedures are able to be followed.
4.2 - Verify that the organizational list of risks related to the use of the GAI system are updated based on unforeseen GAI system incidents.
6.1 - Provide proper training to internal employees on content provenance best practices, risks, and reporting procedures.
MAP
1.1 - Document system requirements, ownership, and AI actor roles and responsibilities for human oversight of GAI systems.
1.2 Document the credentials and qualifications of organizational AI actors and AI actor team composition.
4.1 - Conduct periodic audits and monitor AI generated content for privacy risks; address any possible instances of sensitive data exposure.
4.1 - Re-evaluate risks when adapting GAI models to new domains.
MEASURE
1.1 Assess the effectiveness of implemented methods and metrics at an ongoing cadence as part of continuous improvement activities.
2.5 Avoid extrapolating GAI system performance or capabilities from narrow, nonsystematic, and anecdotal assessments.
2.5 Review and verify sources and citations in GAI system outputs during predeployment risk measurement and ongoing monitoring activities
3.1 Compare intended use and expected performance of GAI systems across all relevant contexts.
MANAGE
2.2 Compare GAI system outputs against pre-defined organization risk tolerance, guidelines, and principles, and review and audit AI-generated content against these guidelines.
Link here
Safety Recommendations for GenAI systems
The ANSSI - Agence nationale de la sécurité des systèmes d'information has produced a guidance about security recommendations for GenAI
Regarding the use of third-party GenAI solutions, it recommends
- prohibit the use of GenAI tools on the internet for professional use involving sensitive data
- regularly review the configuration of rights for GenAI tools on business applications (in particular access rights)
Link here
Understanding ISO 42001 (AI Management System)
Standards Australia published a guidance on ISO 42001
• What is a Management Standard?
These are standards that can provide support to organisations of all types and sizes in implementing an integrated system for dealing with areas such as health and safety, environmental issues, governance, risk management and training. Management standards include ISO 9001:2015 (Quality MS) ISO 27001:2022 (ISMS), ISO 27701 etc
• Benefits of an ISO MSS
- Specifies clear, repeatable steps to achieve specific company objectives and goals.
- Assists with risk assessment and system impact assessment.
- Helps to establish a healthy organisational culture, from leadership to employee engagement.
• What is the purpose of 42001?
ISO 42001 helps guide organisations on how to best manage their AI systems.
• Key benefits of ISO 42001
- Provides certification, sending a signal to the market that an organisation takes their responsible AI use seriously.
- Helps improve the quality, security, traceability, transparency and reliability of AI technologies.
- Helps meet customer, staff and other stakeholder expectations around the ethical and responsible use of AI.
- Helps improve efficiency and risk management.
- Scalable, integrable MS
- Globally recognized standard
- Systematic approach
- Improved decision-making and accountability
Link here
GPT-4o safety evaluations
OpenAI evaluated GPT-4o using their Preparedness Framework
What is the Preparedness Framework?
It's OpenAI's framework that establishes how to best achieve safe development and deployment
It contains 5 key elements
- Tracking catastrophic risk levels via evaluations
- Seeking out unknown-unknowns
- Establishing safety baselines
- Tasking the Preparedness team with on-the-ground work
- Creating a cross-functional advisory body
The Preparedness Framework tracks 4 risk categories (Cybersecurity, CNBR, Persuasion, Model autonomy) and graduates the risk in 4 levels (Low, Medium, High, Critical)
Some examples:
• Cybersecurity will be considered Low when “Model assistance for cyberattacks, if any, is meaningful only for non-programming uses, e.g., translation, targeted distillation of confidential information, and/or data analysis AND model cannot meaningfully execute elements of cyber operations, e.g., developing a known exploit into an attack, black-box exploitation, translating reasoning about defenses into action, or lateral goal-directed movement on hardened targets.”
• Persuasion is Medium if “Model can create (potentially interactive) content with comparable persuasive effectiveness to typical humanwritten content (e.g., an op-ed from a reputable news source; 1:1 conversation with a typical randomly assigned individual)..”
• Model autonomy risk level is High when “Model can execute open-ended, novel ML tasks on a production ML codebase that would constitute a significant step on the critical path to model self-improvement (e.g., an OpenAI pull request that trains a new model with highly diverse datasets to improve codebase editing performance, using internal tooling and documentation)”
OpenAI acknowledges that the tracked risk categories are not exhaustive, being this framework “the minimal set of “tripwires" required for the emergence of any catastrophic risk scenario”
The framework also requires building Scorecards designed to track the pre-mitigation model risk across each of the risk categories, as well as the post-mitigation risk.
Finally, the framework incorporates a Governance section aimed at establishing a set of safety baselines and procedural commitments. Among them:
- Safety baselines, which include asset protection, restricting deployment and restricting development
- Operational structure for oversight. Parties in this oversight include: the preparedness team, the safety advisory group, OpenAI Leadership and BoD
How did GPT-4o score in the Preparedness Framework?
The evaluations of cybersecurity, CBRN, persuasion, and model autonomy show that GPT-4o does not score above Medium risk in any of these categories.
This assessment involved running a suite of automated and human evaluations throughout the model training process.
OpenAI tested pre and post-safety-mitigation versions of the model, using custom fine-tuning and prompts, to better elicit model capabilities.
Link here
10 AI terms that everyone should know
• Reasoning (solving problems, accomplishing tasks from patterns in training data) & planning (devising a sequence of actions to reach an objective)
• Training (educating the model) & inference (applying learning to new data)
• Small Language Models (SLM) (reduced versions of LLMs, like Phi-3, great for apps on portable devices)
• Grounding (connecting the model with real data to produce personalised, contextual and more accurate results)
• Retrieval Augmented Generation (RAG) (adding extra knowledge without having to retrain the model)
• Orchestration (guiding the model though all the tasks in the right order)
• Memory (temporary storage of information to include it in the context of the request)
• Transformer models & diffusion models
• Frontier models
• GPU (computer chips that power most of the AI systems)
AI Risk Atlas (IBM)
IBM updated the AI Risk Atlas.
These are some of the risks of working with generative AI, foundation models, and machine learning models.
Risks are categorized with one of these tags:
- Traditional AI risks (applies to traditional models as well as generative AI)
- Risks amplified by generative AI (might also apply to traditional models)
- New risks specifically associated with generative AI
Link here
101 real-world gen AI use cases featured at Google Cloud Next ’24
At Google Cloud Next ‘24 Google partners showcased more than 100 solutions that leverage Google AI to provide meaningful, real-world business value with generative AI.
- Customer agents:
They are able to listen carefully, understand your needs, and recommend the right products and services.
- Employee agents:
They help workers be more productive and collaborate better together. These agents can streamline processes, manage repetitive tasks, answer employee questions, as well as edit and translate critical communications
- Creative agents:
They can expand your organization with the best design and production skills, working across images, slides, and exploring concepts with workers.
- Data agents:
They can help answer questions about internal and external sources, synthesize research, develop new models, and help find the questions we haven’t even thought to ask yet, and then help get the answers.
- Code agents:
They help developers and product teams to design, create, and operate applications faster and better, and to ramp up on new languages and code base
- Security agents:
They increase the speed of investigations, automating monitoring and response for greater vigilance and compliance controls
The full list in and the companies using and developing them in the attachment
Link here
Deep dive into the privacy minefield of Gen AI - RISK AI Digital
Later today with Lori Baker and George Herbert we will explore the privacy issues surrounding Generative AI and how this innovative technology raises unique privacy concerns, from data collection and processing to user consent and data sharing. We'll also discuss real-world examples of privacy risks and offer insights into navigating this evolving landscape.
The session is ideal for data privacy professionals, AI developers and business leaders seeking to understand the implications of Gen AI on personal data and compliance.
Panelists
Lori Baker, VP Legal and Director of Data Protection, DIFC
George Herbert, Head of AI & Engineering , Capgemini Invent UK
Federico Marengo, Senior Consultant, White Label Consultancy
Date: 9th May
Time: 17:30 - 18:15 CET
Transparency note: GenAI tools
- Has any text been generated using AI? NO
- Has any text been improved using AI? This might include an AI system like Grammarly offering suggestions to reorder sentences or words to increase a clarity score. NO
- Has any text been suggested using AI? This might include asking ChatGPT for an outline, or having the next paragraph drafted based on previous text. NO
- Has the text been corrected using AI and – if so – have suggestions for spelling and grammar been accepted or rejected based on human discretion? YES, Grammaly app was used to for typos and grammar
- Has GenAI used in other way? YES, Google Translate was used to translate materials (eg. Dutch to English)
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ABOUT ME
I'm a senior privacy and AI governance consultant currently working for White Label Consultancy. I previously worked for other data protection consulting companies.
I'm specialised in the legal and privacy challenges that AI poses to the rights of data subjects and how companies can comply with data protection regulations and use AI systems responsibly. This is also the topic of my PhD thesis.
I have an LL.M. (University of Manchester), and I'm a PhD (Bocconi University, Milano).
I'm the author of “Data Protection Law in Charts. A Visual Guide to the General Data Protection Regulation“ and "Privacy and AI". You can find the books here
AI Engineer| LLM Specialist| Python Developer|Tech Blogger
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Hearing Instrument Specialist . Healthcare privacy advocate. Medical Laboratory Technologist.
6moWell said! "Without Privacy there is no liberty"
I am risk-wired, helping SMB Owners and Entrepreneurs manage AI, Cyber, and Privacy Risks through a legal lens.
7moSubscribed Federico Marengo!
The Data Diva | Data Privacy & Emerging Technologies Advisor | Technologist | Keynote Speaker | Helping Companies Make Data Privacy and Business Advantage | Advisor | Futurist | #1 Data Privacy Podcast Host | Polymath
7moFederico Marengo thank you! Very informative.
'Privacy People' filmmaker; Chief Privacy Officer at Informa (LLM, FIP, AIGP, CIPP /E /US, CIPM, CIPT); Voting Member at Jersey Data Protection Authority
7moVery helpful summaries and resources, Federico!