Artificial Intelligence
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.
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.
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
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
- 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.
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
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.
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
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 | The 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 prioritizing 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. |
Demographics vs Psychographics for Machine Learning
When preparing data for data science, data mining or machine learning projects you will create a data set that describes the various characteristics of the subject or case record. Each attribute will contain some descriptive information about the subject and is related to the target variable in some way.
In addition to these attributes, the data set will be enriched with various other internal/external data to complete the data set.
Some of the attributes in the data set can be grouped under the heading of Demographics. Demographic data contains attributes that explain or describe the person or event each case record is focused on. For example, if the subject of the case record is based on Customer data, this is the “Who” the demographic data (and features/attributes) will be about. Examples of demographic data include:
- Age range
- Marital status
- Number of children
- Household income
- Occupation
- Educational level
These features/attributes are typically readily available within your data sources and if they aren’t then these name be available from a purchased data set.
Additional feature engineering methods are used to generate new features/attributes that express meaning is different ways. This can be done by combining features in different ways, binning, dimensionality reduction, discretization, various data transformations, etc. The list can go on.
The aim of all of this is to enrich the data set to include more descriptive data about the subject. This enriched data set will then be used by the machine learning algorithms to find the hidden patterns in the data. The richer and descriptive the data set is the greater the likelihood of the algorithms in detecting the various relationships between the features and their values. These relationships will then be included in the created/generated model.
Another approach to consider when creating and enriching your data set is move beyond the descriptive features typically associated with Demographic data, to include Pyschographic data.
Psychographic data is a variation on demographic data where the feature are about describing the habits of the subject or customer. Demographics focus on the “who” while psycographics focus on the “why”. For example, a common problem with data sets is that they describe subjects/people who have things in common. In such scenarios we want to understand them at a deeper level. Psycographics allows us to do this. Examples of Psycographics include:
- Lifestyle activities
- Evening activities
- Purchasing interests – quality over economy, how environmentally concerned are you
- How happy are you with work, family, etc
- Social activities and changes in these
- What attitudes you have for certain topic areas
- What are your principles and beliefs
The above gives a far deeper insight into the subject/person and helps to differentiate each subject/person from each other, when there is a high similarity between all subjects in the data set. For example, demographic information might tell you something about a person’s age, but psychographic information will tell you that the person is just starting a family and is in the market for baby products.
I’ll close with this. Consider the various types of data gathering that companies like Google, Facebook, etc perform. They gather lots of different types of data about individuals. This allows them to build up a complete and extensive profile of all activities for individuals. They can use this to deliver more accurate marketing and advertising. For example, Google gathers data about what places to visit throughout a data, they gather all your search results, and lots of other activities. They can do a lot with this data. but now they own Fitbit. Think about what they can do with that data and particularly when combined with all the other data they have about you. What if they had access to your medical records too! Go Google this ! You will find articles about them now having access to your health records. Again combine all of the data from these different data sources. How valuable is that data?
Ethics in the AI, Machine Learning, Data Science, etc Era
Ethics is one of those topics that everyone has a slightly different definition or view of what it means. The Oxford English dictionary defines ethics as, ‘Moral principles that govern a person’s behaviour or the conducting of an activity‘.
As you can imagine this topic can be difficult to discuss and has many, many different aspects.
In the era of AI, Machine Learning, Data Science, etc the topic of Ethics is finally becoming an important topic. Again there are many perspective on this. I’m not going to get into these in this blog post, because if I did I could end up writing a PhD dissertation on it.
But if you do work in the area of AI, Machine Learning, Data Science, etc you do need to think about the ethical aspects of what you do. For most people, you will be working on topics where ethics doesn’t really apply. For example, examining log data, looking for trends, etc
But when you start working of projects examining individuals and their behaviours then you do need to examine the ethical aspects of such work. Everyday we experience adverts, web sites, marketing, etc that has used AI, Machine Learning and Data Science to delivery certain product offerings to us.
Just because we can do something, doesn’t mean we should do it.
One particular area that I will not work on is Location Based Advertising. Imagine walking down a typical high street with lots and lots of retail stores. Your phone vibrates and on the screen there is a message. The message is a special offer or promotion for one of the shops a short distance ahead of you. You are being analysed. Your previous buying patterns and behaviours are being analysed, Your location and direction of travel is being analysed. Some one, or many AI applications are watching you. This is not anything new and there are lots of examples of this from around the world.
But what if this kind of Location Based Advertising was taken to another level. What if the shops had cameras that monitored the people walking up and down the street. What if those cameras were analysing you, analysing what clothes you are wearing, analysing the brands you are wearing, analysing what accessories you have, analysing your body language, etc. They are trying to analyse if you are the kind of person they want to sell to. They then have staff who will come up to you, as you are walking down the street, and will have customised personalised special offers on products in their store, just for you.
See the segment between 2:00 and 4:00 in this video. This gives you an idea of what is possible.
Are you Ok with this?
As an AI, Machine Learning, Data Science professional, are you Ok with this?
The technology exists to make this kind of Location Based Marketing possible. This will be an increasing ethical consideration over the coming years for those who work in the area of AI, Machine Learning, Data Science, etc
Just because we can, doesn’t mean we should!
Oralytics 
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