AI

AI Sandboxes – EU AI Regulations

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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

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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
  • Accountability

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

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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.

Ireland AI Strategy (2021)

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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

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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

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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

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

Singapore

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.

USA

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
  • 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.

Canada

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

Truth, Fairness & Equality in AI – US Federal Trade Commission

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Over the past few months we have seen a growing level of communication, guidelines, regulations and legislation for the use of Machine Learning (ML) and Artificial Intelligence (AI). Where Artificial Intelligence is a superset containing all possible machine or computer generated or apply intelligence consisting of any logic that makes a decision or calculation. 

Deep Learning: The Latest Trend In AI And ML | Qubole

Although the EU has been leading the charge in this area, other countries have been following suit with similar guidelines and legislation.


There has been several examples of this in the USA over the past couple of years. Some of this has been prefaced by the debates and issues around the use of facial recognition. Some States in USA have introduced laws to control what can and cannot be done, but, at time of writing, where is no federal law governing the whole of USA.

In April 2021, the US Federal Trade Commission published and article on titled ‘Aiming for truth, fairness, and equity in Company’s use of AI‘.

They provide guidelines on how to build AI applications while avoiding potential issues such as bias and unfair outcomes, and at the same time incorporating transparency. In addition to the recommendations in the report, they point to three laws (which have been around for some time) which are important for developers of AI applications. These include:

  • Section 5 of the FTC Act: The FTC Act prohibits unfair or deceptive practices. That would include the sale or use of – for example – racially biased algorithms.
  • Fair Credit Reporting Act: The FCRA comes into play in certain circumstances where an algorithm is used to deny people employment, housing, credit, insurance, or other benefits.
  • Equal Credit Opportunity Act: The ECOA makes it illegal for a company to use a biased algorithm that results in credit discrimination on the basis of race, color, religion, national origin, sex, marital status, age, or because a person receives public assistance.

These guidelines aims for truthfully, fairly and equitably. With these covering the technical and non-technical side of AI applications. The guidelines include:

  • Start with the right direction: Get your data set right, what is missing, is it balanced, what’s missing, etc. Look at how to improve the data set and address any shortcomings, and this may limit you use model
  • Watch out of discriminatory outcomes: Are the outcomes biased? If it works for you data set and scenario, will it work in others eg. Applying the model in a different hospital? Regular and detail testing is needed to ensure no discrimination gets included
  • Embrace transparency and independence: Think about how to incorporate transparency from the beginning of the AI project. Use international best practice and standards, have independent audits and publish results, by opening the data and source code to outside inspection.
  • Don’t exaggerate what you algorithm can do or whether it can deliver fair or unbiased results: That kind of says it all really. Under the FTC Act, your statements to business customers and consumers must be truthful, no-deceptive and backed up by evidence. Typically with the rush to introduce new technologies and products there can be a tendency to over exaggerate what it can do. Don’t do this
  • Tell the truth about how you use data: Be careful about what data you used and how you got this data. For example, Facebook using facial recognition software on pictures default, when they asked for your permission but ignored what you said. Misrepresentation of what the customer/consumer was told.
  • Do more good than harm: A practice is unfair if it causes more harm than good. Making decisions based on race, color, religion, sex, etc.  If the model causes more harm than good, if it causes or is likely to cause substantial injury to consumers that I not reasonably avoidable by consumers and not outweighed by countervailing benefits to consumers or to competition, their model is unfair.
  • Hold yourself accountable: If you use AI, in any form, you will be held accountable for the algorithm’s performance.

Some of these guidelines build upon does from April 2020, on Using Artificial Intelligence and Algorithms, where there is a focus on fair use of data, transparency of data usage, algorithms and models, ability to clearly explain how a decision was made, and ensure all decisions made are fair and unbiased

Working with AI products and applications can be challenging in many different ways. Most of the focus, information and examples is about building these. But that can be the easy part. With the growing number of legal aspects from different regions around the world the task of managing AI products and applications is becoming more and more complicated.


The EU AI Regulations supports the role of person to oversee these different aspects, and this is something we will see job adverts for very very soon, no matter what country or region you live in. The people in these roles will help steer and support companies through this difficult and evolving area, to ensure compliance with local as well and global compliance and legal requirements.

Responsible AI: Principles & Standards around the World

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During 2019 there was been a increase awareness of AI and the need for Responsible AI. During 2020 (and beyond) we will see more and more on this topic. To get you started on some of the details and some background reading, here are links to various Principles and Standards for Responsible AI from around the World.

Standard/Principles Description
EU AI Ethics Guidelines T​he Ethics Guidelines for Trustworthy Artificial Intelligence developed by EU High-Level Expert Group on AI ​highlights that trustworthy AI should be lawful, ethical and robust. Puts forward seven key requirements for AI systems should meet in order to be deemed trustworthy, including among others ​diversity, non-discrimination, societal and environmental well-being, transparency and accountability.
OECD principles on Artificial Intelligence OECD’s ​member​ countries along with partner countries adopted the first ever set of intergovernmental policy guidelines on ​AI​, agreeing to uphold international standards ​that aim to ensure AI systems are ​designed in a way that respects the rule of law, human rights, democratic values and diversity. They emphasize that AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being.
CoE: Human Rights impacts of Algorithms Council of Europe draft recommendation on the human rights impacts of algorithmic AI systems, released for consultation in August 2019 and to be adopted in early 2020. ​The document explicitly refers to the UN Guiding Principles on Business and Human Rights as a guidance for due diligence process and ​Human ​Rights Impact Assessments.
IEEE Global Initiative: ​Ethically Aligned Design Ethically Aligned Design (EAD) Document is created ​​to ​educate a broader public ​and to inspire ​academics, engineers, policy makers and manufacturers of autonomous and intelligent systems​ to take action ​on prioritiz​ing ethical considerations​​.​ The general principles for AI design, manufacturing and use include: human rights, wellbeing, ​data agency, effectiveness, transparency, accountability, awareness of misuse, competence. ​The unique IEEE P7000 Standards series address specific issues at the intersection of technology and ethics​ and aimed to empower innovation across borders and enable societal benefit.
UN Sustainable Development Goals The UN Sustainable Goals include the annual ​AI for Good Global Summit is the leading UN platform for global and inclusive dialogue on how artificial intelligence could help accelerate progress towards the ​​Global Goals.
UN Business ​and Human Rights The UN Guiding Principles on Business and Human Rights (UNGPs)gives a framework offering a roadmap​ to navigate responsibility-related challenges, rapid ​technological disruption and rising ​inequality, business has a ​unique opportunity ​to implement​ human-centered innovation by taking into account ​social, ethical​ and human rights implications of AI.
EU Collaborative Platforms and Social Learning Several EU countries have ​​articulated their ambitions related to artificial intelligence, it is of paramount importance to find your unique voice, ​​track and join ​essential conversations, strategically engage in collective efforts and leave meaningful digital footprint.​

Always watching, always listening. Be careful with your data

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The saying ‘Big Brother is Watching’ has been around a long time and typically gets associated with government organisations. But over the past few years we have a few new Big Brothers appearing. These are in the form of Google and Facebook and a few others.

These companies gather lots and lots. Some companies gather enormous amounts of data. This data will include details of your interactions with the companies through various websites, applications, etc. But some are gathering data in ways that you might not be aware. For example, take this following video. Data is being gathered about what you do and where you go even if you have disconnected your phone.

Did you know this kind of data was being gathered about you?

Just think of what they could be doing with that data, that data you didn’t know they were gathering about you. Companies like these generate huge amounts of income from selling advertisements and the more data they have about individuals the more the can understand what they might be interested. The generate customer profiles and sell expensive advertising based on having these very detailed customer profiles.

But it doesn’t stop there. Recently Google bought Fitbit. Just think about what they can do now. Combining their existing profiles of you as a person with you activities throughout every day, week and month. Just think about how various health and insurance companies would love to have this data. Yes they would and companies like Google would be able to charge these companies even more money for this level of detail on individuals/customers.

But it doesn’t stop there. There have been lots of reports of various apps sharing health and other related data with various companies, without their customers being aware this is happening.

What about Google Assistant? In a recent article by MIT Technology Review title Inside Amazon’s plan for Alexa to run your entire life, they discuss how Alexa can be used to control virtually everything. In this article Alexa’s cheif scientist say “plan is for the voice assistant to move from passive to proactive interactions. Rather than wait for and respond to requests, Alexa will anticipate what the user might want. The idea is to turn Alexa into an omnipresent companion that actively shapes and orchestrates your life. This will require Alexa to get to know you better than ever before.”  When combined with other products this will allow “these new products let Alexa listen to and log data about a dramatically larger portion of your life“.

Just imagine if Google did the same with their Google Assistant!  Big Brother isn’t just Watching, they are also Listening!

There has been some recent report of Google looking to get into Banking by offering checking accounts. The project, code-named Cache, is due to launch in 2020. Google has partnered with Citigroup and a credit union at Stanford University, which will administer the accounts. Users will be able to access their accounts through Google’s digital payment platform, Google Pay.

And there are the reports of Google having access to the health records of over 50 million people. In addition to this, Google has signed a deal with Ascension, the second-largest hospital system in the US, to collect and analyze millions of Americans’ personal health data. Ascension operates in 150 hospitals in 21 states.

What if they also had access to your banking details and spending habits? Google is looking at different options to extend financial products from the google pay into more main stream banking. There has been some recent report of them looking at offering current accounts.

I won’t go discussing their attempts at Ethics and their various (failed) attempts at establishing and Ethics Advisory Board. This has been well documented elsewhere.

Things are getting a bit scary and the saying ‘Big Brother is Watching You’, is very, very true.

In the ever increasing connected world, all of us have a responsibility to know what data companies are gathering on us. We need to decide how comfortable we are with this and if you aren’t then you need to take steps to ensure you protect yourself. Maybe part of this protection requires us to become less connected, stop using some apps, turn off more notification, turn off updates, turn off tracking, etc

While taking each product or offering individually, it may seem ok to us for Google and other companies to offer such services and to analyze our data to provide a better service. But for most people the issues arise when each of these products start to be combined. By doing this they get to have greater access and understanding our our data and our behaviors. What role does (digital) ethics play in all of this? This is something for the company and the employees to decide where things should stop. But when/how do you decide this? when do you/they know things have gone too far? how can you undo some of this work to go back to an acceptable level? what is an acceptable level and how do you define this?

As yo can see there are lots of things to consider and a vital component is the role of (digital) ethics. All organizations who process and analyze data need to have an ethics board and ethics needs to be a core part of every project. To support this everyone needs more training and awareness of ethics and what is acceptable or not.