AI Liability Act
Over the past few weeks we have seem a number of new Artificial Intelligence (AI) Acts or Laws, either being proposed or are at an advanced stage of enactment. One of these is the EU AI Liability Act (also), and is supposed be be enacted and work hand-in-hand with the EU AI Act.
There are different view or focus perspectives between these two EU AI acts. For the EU AI Act, the focus is from the technical perspective and those who develop AI solutions. On the other side of things is the EU AI Liability Act whose perspective is from the end-user/consumer point.
The aim of the EU AI Liability Act is to create a framework for trust in AI technology, and when a person has been harmed by the use of the AI, provides a structure to claim compensation. Just like other EU laws to protect the consumers from defective or harmful products, the AI Liability Act looks to do similar for when a person is harmed in some way by the use or application of AI.
Most of the examples given for how AI might harm a person includes the use of robotics, drones, and when AI is used in the recruitment process, where is automatically selects a candidate based on the AI algorithms. Some other examples include data loss from tech products or caused by tech products, smart-home systems, cyber security, products where people are selected or excluded based on algorithms.
Harm can be difficult to define, and although some attempt has been done to define this in the Act, additional work is needed to by the good people refining the Act, to provide clarifications on this and how its definition can evolve post enactment to ensure additional scenarios can be included without the need for updates to the Act, which can be a lengthy process. A similar task is being performed on the list of high-risk AI in the EU AI Act, where they are proposing to maintain a webpages listing such.
Vice-president for values and transparency, Věra Jourová, said that for AI tech to thrive in the EU, it is important for people to trust digital innovation. She added that the new proposals would give customers “tools for remedies in case of damage caused by AI so that they have the same level of protection as with traditional technologies”
Didier Reynders, the EU’s justice commissioner says, “The new rules apply when a product that functions thanks to AI technology causes damage and that this damage is the result of an error made by manufacturers, developers or users of this technology.
The EU defines “an error” in this case to include not just mistakes in how the A.I. is crafted, trained, deployed, or functions, but also if the “error” is the company failing to comply with a lot of the process and governance requirements stipulated in the bloc’s new A.I. Act. The new liability rules say that if an organization has not complied with their “duty of care” under the new A.I. Act—such as failing to conduct appropriate risk assessments, testing, and monitoring—and a liability claim later arises, there will be a presumption that the A.I. was at fault. This creates an additional way of forcing compliance with the EU AI Act.
The EU Liability Act says that a court can now order a company using a high-risk A.I. system to turn over evidence of how the software works. A balancing test will be applied to ensure that trade secrets and other confidential information is not needlessly disclosed. The EU warns that if a company or organization fails to comply with a court-ordered disclosure, the courts will be free to presume the entity using the A.I. software is liable.
The EU Liability Act will go through some changes and refinement with the aim for it to be enacted at the same time as the EU AI Act. How long will this process that is a little up in the air, considering the EU AI Act should have been adopted by now and we could be in the 2 year process for enactment. But the EU AI Act is still working its way through the different groups in the EU. There has been some indications these might conclude in 2023, but lets wait and see. If the EU Liability Act is only starting the process now, there could be some additional details if the EU wants both Acts to be effective at the same time.
Oracle OCI AI Services
Oracle Cloud have been introducing new AI Services over the past few months, and we see a few more appearing over the coming few months. When you look at the list you might be a little surprised that these are newly available cloud services from Oracle. You might be surprised for two main reasons. Firstly, AWS and Google have similar cloud services available for some time (many years) now, and secondly, Oracle started talking about having these cloud services many years ago. It has taken some time for these to become publicly available. Although some of these have been included in other applications and offerings from Oracle, so probably they were busy with those before making them available as stand alone services.
These can be located in your Oracle Cloud account from the hamburger menu, as shown below
As you can see most of these AI Services are listed, except for the OCI Forecasting, which is due to be added “soon”. We can also expect to have an OCI Translation services and possibly some additional ones.
- OCI Language: This services can work with over 75 languages and allows you to detect and perform knowledge extraction from the text to include entity identification and labelling, classification of text into more than 600 categories, sentiment analysis and key phrase extraction. This can be used automate knolwedge extraction from customer feedback, product reviews, discussion forums, legal documents, etc
- OCI Speech: Performs Speech to Text, from live streaming of speech, audio and video recordings, etc creating a transcription. It works across English, Spanish and Portuguese, with other languages to be added. A nice little feature includes Profanity filtering, allowing you to tag, remove or mask certain words
- OCI Vision: This has two parts. The first is for processing documents, and is slightly different to OCI Language Service, in that this service looks at processing text documents in jpeg, pdf, png and tiff formats. Text information extraction is performed identifying keep terms, tables, meta-data extraction, table extraction etc. The second part of OCI Vision deals with image analysis and extracting key information from the image such as objects, people, text, image classification, scene detection, etc. You can use either the pretrained models or include your own models.
- OCI Anomaly Detection: Although anomaly detection is available via algorithms in the Database and OCI Data Science offerings, this new services allow for someone with little programming experience to utilise an ensemble of models, including the MSET algorithm, to provide greater accuracy with identifying unusual patterns in the data.
Note: I’ve excluded some services from the above list as these have been available for some time now or have limited AI features included in them. These include OCI Data Labelling, OCI Digital Assistant.
Some of these AI Services, based on the initial release, have limited functionality and resources, but this will change over time.
NATO AI Strategy
Over the past 18 months there has been wide spread push buy many countries and geographic regions, to examine how the creation and use of Artificial Intelligence (AI) can be regulated. I’ve written many blog posts about these. But it isn’t just government or political alliances that are doing this, other types of organisations are also doing so.
NATO, the political and (mainly) military alliance, has also joined the club. They have release a summary version of their AI Strategy. This might seem a little strange for this type of organisation to do something like this. But if you look a little closer NATA also says they work together in other areas such as Standardisation Agreements, Crisis Management, Disarmament, Energy Security, Clime/Environment Change, Gender and Human Security, Science and Technology.
In October/November 2021, NATO formally adopted their Artificial Intelligence (AI) Strategy (for defence). Their AI Strategy outlines how AI can be applied to defence and security in a protected and ethical way (interesting wording). Their aim is to position NATO as a leader of AI adoption, and it provides a common policy basis to support the adoption of AI System sin order to achieve the Alliances three core tasks of Collective Defence, Crisis Management and Cooperative Security. An important element of the AI Strategy is to ensure inter-operability and standardisation. This is a little bit more interesting and perhaps has a lessor focus on ethical use.
NATO’s AI Strategy contains the following principles of Responsible use of AI (in defence):
- Lawfulness: AI applications will be developed and used in accordance with national and international law, including international humanitarian law and human rights law, as applicable.
- Responsibility and Accountability: AI applications will be developed and used with appropriate levels of judgment and care; clear human responsibility shall apply in order to ensure accountability.
- Explainability and Traceability: AI applications will be appropriately understandable and transparent, including through the use of review methodologies, sources, and procedures. This includes verification, assessment and validation mechanisms at either a NATO and/or national level.
- Reliability: AI applications will have explicit, well-defined use cases. The safety, security, and robustness of such capabilities will be subject to testing and assurance within those use cases across their entire life cycle, including through established NATO and/or national certification procedures.
- Governability: AI applications will be developed and used according to their intended functions and will allow for: appropriate human-machine interaction; the ability to detect and avoid unintended consequences; and the ability to take steps, such as disengagement or deactivation of systems, when such systems demonstrate unintended behaviour.
- Bias Mitigation: Proactive steps will be taken to minimise any unintended bias in the development and use of AI applications and in data sets.
By acting collectively members of NATO will ensure a continued focus on interoperability and the development of common standards.
Some points of interest:
- Bias Mitigation efforts will be adopted with the aim of minimising discrimination against traits such as gender, ethnicity or personal attributes. However, the strategy does not say how bias will be tackled – which requires structural changes which go well beyond the use of appropriate training data.
- The strategy also recognises that in due course AI technologies are likely to become widely available, and may be put to malicious uses by both state and non-state actors. NATO’s strategy states that the alliance will aim to identify and safeguard against the threats from malicious use of AI, although again no detail is given on how this will be done.
- Running through the strategy is the idea of interoperability – the desire for different systems to be able to work with each other across NATO’s different forces and nations without any restrictions.
- What about Autonomous weapon systems? Some members do not support a ban on this technology.
- Has similar wording to the principles adopted by the US Department of Defense for the ethical use of AI.
- Wants to make defence and security a more attractive to private sector and academic AI developers/researchers.
- NATO principles have no coherent means of implementation or enforcement.
AI Sandboxes – EU AI Regulations
The EU AI Regulations provides a framework for placing on the market and putting into service AI system in the EU. One of the biggest challenges most organisations will face will be how they can innovate and develop new AI systems while at the same time ensuring they are compliant with the regulations. But a what point do you know you are compliant with these new AI Systems? This can be challenging and could limit or slow down the development and deployment of such systems.
The EU does not want to limit or slow down such innovations and want organisations to continually research, develop and deploy new AI. To facilitate this the EU AI Regulations contains a structure under which this can be achieved.
Section or Title of EU AI Regulations contains Articles 53, 54, and 55 to support the development of new AI systems by the use of Sandboxes. We have already seen examples of these being introduced by the UK and Norwegian Data Protection Commissioners.
A Sandbox “provides a controlled environment that facilitates the development, testing and validation of innovative AI systems for a limited time before their placement on the market or putting into
service pursuant to a specific plan.“
Sandboxes are stand alone environments to allow the exploration and development of new AI solutions, which may or may not include some risky use of customer data or other potential AI outcomes which may not be allowed under the regulations. It becomes a controlled experiment lab for the AI team who are developing and testing a potential AI System and can do so under real world conditions. The Sandbox gives a “safe” environment for this experimental work.
The Sandbox are to be established by the Competent Authorities in each EU country. In Ireland the Competent Authority seems to be the Data Protection Commissioner, and this may be similar in other countries. As you can imagine, under the current wording of the EU AI Regulations this might present some challenges for the both the Competent Authority and also for the company looking to develop an AI solution. Firstly, does the Competent Authority need to provide sandboxes for all companies looking to develop AI, and each one of these companies may have several AI projects. This is a massive overhead for the Competent Authority to provide and resource. Secondly, will companies be willing to setup a self-contained environment, containing customer data, data insights, solutions with potential competitive advantage, etc in a Sandbox provided by the Competent Authority. The technical infrastructure used could be hosting many Sandboxes, with many competing companies using the same infrastructure at the same time. This is a big ask for the companies and the Competent Authority.
Let’s see what really happens regarding the implementation of the Sandboxes over the coming years, and how this will be defined in the final draft of the Regulations.
Article 54 defines additional requirements for the processing of personal data within the Sandbox.
- Personal Data being used is required, and can be fulfilled by processing anonymized, synthetic or other non-personal data. Even if it has been collected for other purposes.
- Continuous monitoring needed to identify any high risk to fundamental rights of the data subject, and response mechanism to mitigate those risks.
- Any personal data to be processed is in a functionally separate, isolated and protected data processing environment under the control of the participants and only authorised persons have access to that data.
- Any personal data processed are not be transmitted, transferred or otherwise accessed by other parties.
- Any processing of personal data does not lead to measures or decisions affecting the data subjects.
- All personal data is deleted once the participation in the sandbox is terminated or the personal data has reached the end of its retention period.
- Full documentation of what was done to the data, must be kept for 1 year after termination of Sandbox, and only to be used for accountability and documentation obligations.
- Documentation of the complete process and rationale behind the training, testing and validation of AI, along with test results as part of technical documentation. (see Annex IV)
- Short Summary of AI project, its objectives and expected results published on website of Competent Authorities
Based on the last bullet point the Competent Authority is required to write am annual report and submit this report to the EU AI Board. The report is to include details on the results of their scheme, good and bad practices, lessons learnt and recommendations on the setup and application of the Regulations within the Sandboxes.
OCED Framework for Classifying of AI Systems
Over the past few months we have seen more and more countries looking at how they can support and regulate the use and development of AI within their geographic areas. For those in Europe, a lot of focus has been on the draft AI Regulations. At the time of writing this post there has been a lot of politics going on in relation to the EU AI Regulations. Some of this has been around the definition of AI, what will be included and excluded in their different categories, who will be policing and enforcing the regulations, among lots of other things. We could end up with a very different set of regulations to what was included in the draft (published April 2021). It also looks like the enactment of the EU AI Regulations will be delayed to the end of 2022, with some people suggesting it would be towards mid-2023 before something formal happens.
I mentioned above one of the things that may or may not change is the definition of AI within the EU AI Regulations. Although primarily focused on the inclusion/exclusion of biometic aspects, there are other refinements being proposed. When you look at what other geographic regions are doing, we start to see some common aspects on their definitions of AI, but we also see some differences. You can imagine the difficulties this will present in the global marketplace and how AI touches upon all/many aspects of most businesses, their customers and their data.
Most of you will have heard of OCED. In recent weeks they have been work across all member countries to work towards a Definition of AI and how different AI systems can be classified. They have called this their OCED Framework for Classifying of AI Systems.
The OCED Framework for Classifying AI System is a tool for policy-makers, regulators, legislators and others so that they can assess the opportunities and risks that different types of AI systems present and to inform their national AI strategies.
The Framework links the technical characteristics of AI with the policy implications set out in the OCED AI Principles which include:
- Inclusive growth, sustainable development and well-being
- Human-centred values and fairness
- Transparency and explainability
- Robustness, security and safety
The framework looks are different aspects depending on if the AI is still within the lab (sandbox) environment or is live in production or in use in the field.
The framework goes into more detail on the various aspects that need to be considered for each of these. The working group have apply the frame work to a number of different AI systems to illustrate how it cab be used.
Check out the framework document where it goes into more detail of each of the criterion listed above for each dimension of the framework.
AI Categories in EU AI Regulations
The EU AI Regulations aims to provide a framework for addressing obligations for the use of AI applications in EU. These applications can be created, operated by or procured by companies both inside the EU and outside the EU, on data/people within the EU. In a previous post I get a fuller outline of the EU AI Regulations.
In this post I will look at proposed categorisation of AI applications, what type of applications fall into each category and what potential impact this may have on the operators of the AI application. The following diagram illustrates the categories detailed in the EU AI Regulations. These will be detailed below.
Let’s have a closer look at each of these categories
Unacceptable Risk (Red section)
The proposed legislation sets out a regulatory structure that bans some uses of AI, heavily regulates high-risk uses and lightly regulates less risky AI systems. The regulations intends to prohibit certain uses of AI which are deemed to be unacceptable because of the risks they pose. These would include deploying subliminal techniques or exploit vulnerabilities of specific groups of persons due to their age or disability, in order to materially distort a person’s behavior in a manner that causes physical or psychological harm; Lead to ‘social scoring’ by public authorities; Conduct ‘real time’ biometric identification in publicly available spaces. A more detailed version of this is:
- Designed or used in a manner that manipulates human behavior, opinions or decisions through choice architectures or other elements of user interfaces, causing a person to behave, form an opinion or take a decision to their detriment.
- Designed or used in a manner that exploits information or prediction about a person or group of persons in order to target their vulnerabilities or special circumstances, causing a person to behave, form an opinion or take a decision to their detriment.
- Indiscriminate surveillance applied in a generalised manner to all natural persons without differentiation. The methods of surveillance may include large scale use of AI systems for monitoring or tracking of natural persons through direct interception or gaining access to communication, location, meta data or other personal data collected in digital and/or physical environments or through automated aggregation and analysis of such data from various sources.
- General purpose social scoring of natural persons, including online. General purpose social scoring consists in the large scale evaluation or classification of the trustworthiness of natural persons [over certain period of time] based on their social behavior in multiple contexts and/or known or predicted personality characteristics, with the social score leading to detrimental treatment to natural person or groups.
There are some exemptions to these when such practices are authorised by law and are carried out [by public authorities or on behalf of public 25 authorities] in order to safeguard public security and are subject to appropriate safeguards for the rights and freedoms of third parties in compliance with Union law.
High Risk (Orange section)
AI systems identified as high-risk include AI technology used in:
- Critical infrastructures (e.g. transport), that could put the life and health of citizens at risk;
- Educational or vocational training, that may determine the access to education and professional course of someone’s life (e.g. scoring of exams);
- Safety components of products (e.g. AI application in robot-assisted surgery);
- Employment, workers management and access to self-employment (e.g. CV-sorting software for recruitment procedures);
- Essential private and public services (e.g. credit scoring denying citizens opportunity to obtain a loan);
- Law enforcement that may interfere with people’s fundamental rights (e.g. evaluation of the reliability of evidence);
- Migration, asylum and border control management (e.g. verification of authenticity of travel documents);
- Administration of justice and democratic processes (e.g. applying the law to a concrete set of facts).
All High risk AI applications will be subject to strict obligations before they can be put on the market:
- Adequate risk assessment and mitigation systems;
- High quality of the datasets feeding the system to minimise risks and discriminatory outcomes;
- Logging of activity to ensure traceability of results
- Detailed documentation providing all information necessary on the system and its purpose for authorities to assess its compliance;
- Clear and adequate information to the user;
- Appropriate human oversight measures to minimise risk;
- High level of robustness, security and accuracy.
These can also be categorised as (i) Risk management; (ii) Data governance; (iii) Technical documentation; (iv) Record keeping (traceability); (v) Transparency and provision of information to users; (vi) Human oversight; (vii) Accuracy; (viii) Cybersecurity robustness.
There will be some exceptions to this when the AI application is required by governmental and law enforcement agencies in certain circumstances.
Limited Risk (Yellow section)
“non-high-risk” AI systems should be encouraged to develop codes of conduct intended to foster the voluntary application of the mandatory requirements applicable to high-risk AI systems.
AI application within this Limited Risk category pose a limited risk, transparency requirements are imposed. For example, AI systems which are intended to interact with natural persons must be designed and developed in such a way that users are informed they are interacting with an AI system, unless it is “obvious from the circumstances and the context of use.”
Minimal Risk (Green section)
The Minimal Risk category a allows for all other AI systems can be developed and used in the EU without additional legal obligations than existing legislation For example, AI-enabled video games or spam filters. Some discussion suggest the vast majority of AI systems currently used in the EU fall into this category, where they represent minimal or no risk.
EU AI Regulations – An Introduction and Overview
In May this year (2021) the EU released a draft version of their EU Artificial Intelligence (AI) Regulations. It was released in May to allow all countries to have some time to consider it before having more detailed discussions on refinements towards the end of 2021, with a planned enactment during 2022.
The regulatory proposal aims to provide AI developers, deployers and users with clear requirements and obligations regarding specific uses of AI. One of the primary aims to ensure people can trust AI and to provide a framework for all to ensure the categorization, use and controls on the safe use of AI.
The draft EU AI Regulations consists of 81 papes, including 18 pages of introduction and background materials, 69 Articles and 92 Recitals (Recitals are the introductory statements in a written agreement or deed, generally appearing at the beginning, and similar to the preamble. They set out a précis of the parties’ intentions; what the contract is for, who the parties are and so on). It isn’t an easy read
One of the interesting things about the EU AI Regulations, and this will have the biggest and widest impact, is their definition of Artificial Intelligence.
Artificial Intelligence System or AI system’ means software that is developed with one or more of the approaches and techniques listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing real or virtual environments. Influencing (real or virtual) environments they interact with. AI systems are designed to operate with varying levels of autonomy. An AI system can be used as a component of a product, also when not embedded therein, or on a stand-alone basis and its outputs may serve to partially or fully automate certain activities, including the provision of a service, the management of a process, the making of a decision or the taking of an action;
When you examine each part of this definition you will start to see how far reaching this regulation are. Most people assume it only affect the IT or AI industry, but it goes much further than that. It affects nearly all industries. This becomes clearer when you look at the techniques listed in Annex I.
ARTIFICIAL INTELLIGENCE TECHNIQUES AND APPROACHES (a) Machine learning approaches, including supervised, unsupervised and reinforcement learning, using a wide variety of methods including deep learning; (b) Logic- and knowledge-based approaches, including knowledge representation, inductive (logic) programming, knowledge bases, inference/deductive engines, (symbolic) reasoning and expert systems; (c)
Statistical approaches, Bayesian estimation, search and optimization methods.
It is (c) that will causes the widest application of the regulations. Statistical approaches to making decisions. But part of the problem with this is what do they mean by Statistical approaches. Could adding two number together be considered statistical, or by performing some simple comparison. This part of the definition will need some clarification, and they do say in the regulations, this list may get expanded over time without needing to update the Articles. This needs to be carefully watched and monitored by all.
At a simple level the regulations gives a framework for defining or categorizing AI systems and what controls need to be put in place to support this. This image below is typically used to represent these categories.
The regulations will require companies to invest a lot of time and money into ensure compliance. These will involve training, supports, audits, oversights, assessments, etc not just initially but also on an annual basis, with some reports estimating an annual cost of several tens of thousands of euro per model per year. Again we can expect some clarifications of this, as the costs of compliance may far exceed the use or financial benefit of using the AI.
At the same time there are many other countries who are looking at introducing similar regulations or laws. Many of these are complementary to each other and perhaps there is a degree of watching each each other are doing. This is to ensure there is a common playing field around the globe. This in turn will make it easier for companies to assess the compliance, to reduce their workload and to ensure they are complying with all requirements.
Most countries within the EU are creating their own AI Strategies, to support development and job creation, all within the boundaries set by the EU AI Regulations. Here are details of Ireland’s AI Strategy.
Watch this space to for more posts and details about the EU AI Regulations.
Ireland AI Strategy (2021)
Over the past year or more there was been a significant increase in publications, guidelines, regulations/laws and various other intentions relating to these. Artificial Intelligence (AI) has been attracting a lot of attention. Most of this attention has been focused on how to put controls on how AI is used across a wide range of use cases. We have heard and read lots and lots of stories of how AI has been used in questionable and ethical scenarios. These have, to a certain extent, given the use of AI a bit of a bad label. While some of this is justified, some is not, but some allows us to question the ethical use of these technologies. But not all AI, and the underpinning technologies, are bad. Most have been developed for good purposes and as these technologies mature they sometimes get used in scenarios that are less good.
We constantly need to develop new technologies and deploy these in real use scenarios. Ireland has a long history as a leader in the IT industry, with many of the top 100+ IT companies in the world having research and development operations in Ireland, as well as many service suppliers. The Irish government recently released the National AI Strategy (2021).
“The National AI Strategy will serve as a roadmap to an ethical, trustworthy and human-centric design, development, deployment and governance of AI to ensure Ireland can unleash the potential that AI can provide”. “Underpinning our Strategy are three core principles to best embrace the opportunities of AI – adopting a human-centric approach to the application of AI; staying open and adaptable to innovations; and ensuring good governance to build trust and confidence for innovation to flourish, because ultimately if AI is to be truly inclusive and have a positive impact on all of us, we need to be clear on its role in our society and ensure that trust is the ultimate marker of success.” Robert Troy, Minister of State for Trade Promotion, Digital and Company Regulation.
The eight different strands are identified and each sets out how Ireland can be an international leader in using AI to benefit the economy and society.
- Building public trust in AI
- Strand 1: AI and society
- Strand 2: A governance ecosystem that promotes trustworthy AI
- Leveraging AI for economic and societal benefit
- Strand 3: Driving adoption of AI in Irish enterprise
- Strand 4: AI serving the public
- Enablers for AI
- Strand 5: A strong AI innovation ecosystem
- Strand 6: AI education, skills and talent
- Strand 7: A supportive and secure infrastructure for AI
- Strand 8: Implementing the Strategy
Each strand has a clear list of objectives and strategic actions for achieving each strand, at national, EU and at a Global level.
Check out the full document here.
Australia New AI Regulations Framework
Over the past few weeks/months we have seen more and more countries addressing the potential issues and challenges with Artificial Intelligence (and it’s components of Statistical Analysis, Machine Learning, Deep Learning, etc). Each country has either adopted into law controls on how these new technologies can be used and where they can be used. Many of these legal frameworks have implications beyond their geographic boundaries. This makes working with such technology and ever increasing and very difficult challenging.
In this post, I’ll have look at the new AI Regulations Framework recently published in Australia.
[I’ve written posts on what other countries had done. Make sure to check those out]
The Australia AI Regulations Framework is available from tech.humanrights.gov.au, is a 240 page report giving 38 different recommendations. This framework does not present any new laws, but provides a set of recommendations for the government to address and enact new legislation.
It should be noted that a large part of this framework is focused on Accessible Technology. It is great to see such recommendations. Apart from the section relating to Accessibility, the report contains 2 main sections addressing the use of Artificial Intelligence (AI) and how to support the implementation and regulation of any new laws with the appointment of an AI Safety Commissioner.
Focusing on the section on the use of Artificial Intelligence, the following is a summary of the 20 recommendations:
Chapter 5 – Legal Accountability for Government use of AI
Introduce legislation to require that a human rights impact assessment (HRIA) be undertaken before any department or agency uses an AI-informed decision-making system to make administrative decisions. When an AI decision is made measures are needed to improve transparency, including notification of the use of AI and strengthening a right to reasons or an explanation for AI-informed administrative decisions, and an independent review for all AI-informed administrative decisions.
Chapter 6 – Legal Accountability for Private use of AI
In a similar manner to governmental use of AI, human rights and accountability are also important when corporations and other non-government entities use AI to make decisions. Corporations and other non-government bodies are encouraged to undertake HRIAs before using AI-informed decision-making systems and individuals be notified about the use of AI-informed decisions affecting them.
Chapter 7 – Encouraging Better AI Informed Decision Making
Complement self-regulation with legal regulation to create better AI-informed decision-making systems with standards and certification for the use of AI in decision making, creating ‘regulatory sandboxes’ that allow for experimentation and innovation, and rules for government procurement of decision-making tools and systems.
Chapter 8 – AI, Equality and Non-Discrimination (Bias)
Bias occurs when AI decision making produces outputs that result in unfairness or discrimination. Examples of AI bias has arisen in in the criminal justice system, advertising, recruitment, healthcare, policing and elsewhere. The recommendation is to provide guidance for government and non-government bodies in complying with anti-discrimination law in the context of AI-informed decision making
Chapter 9 – Biometric Surveillance, Facial Recognition and Privacy
There is lot of concern around the use of biometric technology, especially Facial Recognition. The recommendations include law reform to provide better human rights and privacy protection regarding the development and use of these technologies through regulation facial and biometric technology (Recommendations 19, 21), and a moratorium on the use of biometric technologies in high-risk decision making until such protections are in place (Recommendation 20).
In addition to the recommendations on the use of AI technologies, the framework also recommends the establishment of a AI Safety Commissioner to support the ongoing efforts with building capacity and implementation of regulations, to monitor and investigate use of AI, and support the government and private sector with complying with laws and ethical requirements with the use of AI.
Regulating AI around the World
Continuing my series of blog posts on various ML and AI regulations and laws, this post will look at what some other countries are doing to regulate ML and AI, with a particular focus on facial recognition and more advanced applications of ML. Some of the examples listed below are work-in-progress, while others such as EU AI Regulations are at a more advanced stage with introduction of regulations and laws.
[Note: What is listed below is in addition to various data protection regulations each country or region has implemented in recent years, for example EU GDPR and similar]
Things are moving fast in this area with more countries introducing regulations all the time. The following list is by no means exhaustive but it gives you a feel for what is happening around the world and what will be coming to your country very soon. The EU and (parts of) USA are leading in these areas, it is important to know these regulations and laws will impact on most AI/ML applications and work around the world. If you are processing data about an individual in these geographic regions then these laws affect you and what you can do. It doesn’t matter where you live.
New Zealand along wit the World Economic Forum (WEF) are developing a governance framework for AI regulations. It is focusing on three areas:
- Inclusive national conversation on the use of AI
- Enhancing the understand of AI and it’s application to inform policy making
- Mitigation of risks associated with AI applications
The Personal Data Protection Commission has released a framework called ‘Model AI Governance Framework‘, to provide a model on implementing ethical and governance issues when deploying AI application. It supports having explainable AI, allowing for clear and transparent communications on how the AI applications work. The idea is to build understanding and trust in these technological solutions. It consists of four principles:
- Internal Governance Structures and Measures
- Determining the Level of Human Involvement in AI-augmented Decision Making
- Operations Management, minimizing bias, explainability and robustness
- Stakeholder Interaction and Communication.
Progress within the USA has been divided between local state level initiatives, for example California where different regions have implemented their own laws, while at a state level there has been attempts are laws. But California is not along with almost half of the states introducing laws restricting the use of facial recognition and personal data protection. In addition to what is happening at State level, there has been some orders and laws introduced at government level.
- Executive Order on Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government
- This provides guidelines to help Federal Agencies with AI adoption and to foster public trust in the technology. It directs agencies to ensure the design, development, acquisition and use of AI is done in a manner to protects privacy, civil rights, and civil liberties. It includes the following actions:
- Principles for the Use of AI in Government
- Common Policy form Implementing Principles
- Catalogue of Agency Use Cases of AI
- Enhanced AI Implementation Expertise
- This provides guidelines to help Federal Agencies with AI adoption and to foster public trust in the technology. It directs agencies to ensure the design, development, acquisition and use of AI is done in a manner to protects privacy, civil rights, and civil liberties. It includes the following actions:
- Government – Facial Recognition and Biometric Technology Moratorium Act of 2020. Limits the use of biometric surveillance systems such as facial recognition systems by federal and state government entities
USA – Washington State
Many of the States in USA have enacted laws on Facial Recognition and the use of AI. There are too many to list here, but go to this website to explore what each State has done. Taking Washington State as an example, it has enacted a law prohibiting the use of facial recognition technology for ongoing surveillance and limits its use to acquiring evidence of serious criminal offences following authorization of a search warrant.
The Privacy Commissioner of Canada introduced the Regulatory Framework for AI, and calls for legislation supporting the benefits of AI while upholding privacy of individuals. Recommendations include:
- allow personal information to be used for new purposes towards responsible AI innovation and for societal benefits
- authorize these uses within a rights-based framework that would entrench privacy as a human right and a necessary element for the exercise of other fundamental rights
- create a right to meaningful explanation for automated decisions and a right to contest those decisions to ensure they are made fairly and accurately
- strengthen accountability by requiring a demonstration of privacy compliance upon request by the regulator
- empower the OPC to issue binding orders and proportional financial penalties to incentivize compliance with the law
- require organizations to design AI systems from their conception in a way that protects privacy and human rights
The above list is just a sample of what is happening around the World, and we are sure to see lots more of this over the next few years. There are lots of pros and cons to these regulations and laws. One of the biggest challenges being faced by people with AI and ML technologies is knowing what is and isn’t possible/allowed, as most solutions/applications will be working across many geographic regions
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