Tag Archives: ethics

International Guidelines for Ethical AI

In the last two months, i.e. in April and May 2019, both the EU Commission and the OECD published guidelines for trustworthy and ethical Artificial Intelligence (AI). In both cases, these are only guidelines and, as such, are not legally binding. Both sets of guidelines were compiled by experts in the field. Let’s have a closer look.

“Why do we need guidelines for trustworthy, ethical AI?” you may ask. Over the last years, there have been multiple calls, from experts, researchers, lawmakers and the judiciary to develop some kind of legal framework or guidelines for ethical AI.  Several cases have been in the news where the ethics of AI systems came into question. One of the problem areas is bias with regard to gender or race, etc. There was, e.g., the case of COMPAS, which is risk assessment software that is used to predict the likelihood of somebody being repeat offender. It turned out the system had a double racial bias, one in favour of white defendants, and one against black defendants. More recently, Amazon shelved its AI HR assistant because it systematically favoured male applicants. Another problem area is privacy, where there are concerns about deep learning / machine learning, and with technologies like, e.g., facial recognition.

In the case of the EU guidelines, another factor is at play as well. Both the US and China have a substantial lead over the EU when it comes to AI technologies. The EU saw its niche in trustworthy and ethical AI.

EU Guidelines

The EU guidelines were published by the EU Commission on 8 April 2019. (Before that, in December 2018, the European Parliament had already published a report in which it asked for a legal framework or guidelines for AI. The EU Parliament suggested AI systems should be broadly designed in accordance with The Three Laws of Robotics). The Commission stated that trustworthy AI should be:

  • lawful, i.e. respecting all applicable laws and regulations,
  • ethical, i.e. respecting ethical principles and values, and
  • robust, both from a technical perspective while taking into account its social environment.

To that end, the guidelines put forward a set of 7 key requirements:

  • Human agency and oversight: AI systems should empower human beings, allowing them to make informed decisions and fostering their fundamental rights. At the same time, proper oversight mechanisms need to be ensured, which can be achieved through human-in-the-loop, human-on-the-loop, and human-in-command approaches
  • Technical Robustness and safety: AI systems need to be resilient and secure. They need to be safe, ensuring a fall-back plan in case something goes wrong, as well as being accurate, reliable and reproducible. That is the only way to ensure that also unintentional harm can be minimized and prevented.
  • Privacy and data governance: besides ensuring full respect for privacy and data protection, adequate data governance mechanisms must also be ensured, taking into account the quality and integrity of the data, and ensuring legitimised access to data.
  • Transparency: the data, system and AI business models should be transparent. Traceability mechanisms can help achieving this. Moreover, AI systems and their decisions should be explained in a manner adapted to the stakeholder concerned. Humans need to be aware that they are interacting with an AI system, and must be informed of the system’s capabilities and limitations.
  • Diversity, non-discrimination and fairness: Unfair bias must be avoided, as it could have multiple negative implications, from the marginalization of vulnerable groups, to the exacerbation of prejudice and discrimination. Fostering diversity, AI systems should be accessible to all, regardless of any disability, and involve relevant stakeholders throughout their entire life circle.
  • Societal and environmental well-being: AI systems should benefit all human beings, including future generations. It must hence be ensured that they are sustainable and environmentally friendly. Moreover, they should consider the environment, including other living beings, and their social and societal impact should be carefully considered.
  • Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes. Auditability, which enables the assessment of algorithms, data and design processes plays a key role therein, especially in critical applications. Moreover, adequate an accessible redress should be ensured.

A pilot project will be launched later this year, involving the main stakeholders. It will review the proposal more thoroughly and provide feedback, upon which the guidelines can be finetuned. The EU also invites interested business to join the European AI Alliance.

OECD

The OECD consists of 36 members, approximately half of which are EU members. Non-EU members include the US, Japan, Australia, New Zealand, South-Korea, Mexico and others. On 22 May 2019, the OECD Member Countries adopted the OECD Council Recommendation on Artificial Intelligence. As is the case with the EU guidelines, these are recommendations that are not legally binding.

The OECD Recommendation identifies five complementary values-based principles for the responsible stewardship of trustworthy AI:

  1. AI should benefit people and the planet by driving inclusive growth, sustainable development and well-being.
  2. AI systems should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards – for example, enabling human intervention where necessary – to ensure a fair and just society.
  3. There should be transparency and responsible disclosure around AI systems to ensure that people understand AI-based outcomes and can challenge them.
  4. AI systems must function in a robust, secure and safe way throughout their life cycles and potential risks should be continually assessed and managed.
  5. Organisations and individuals developing, deploying or operating AI systems should be held accountable for their proper functioning in line with the above principles.

Consistent with these value-based principles, the OECD also provides five recommendations to governments:

  1. Facilitate public and private investment in research & development to spur innovation in trustworthy AI.
  2. Foster accessible AI ecosystems with digital infrastructure and technologies and mechanisms to share data and knowledge.
  3. Ensure a policy environment that will open the way to deployment of trustworthy AI systems.
  4. Empower people with the skills for AI and support workers for a fair transition.
  5. Co-operate across borders and sectors to progress on responsible stewardship of trustworthy AI.

As you can see, many of the fundamental principles are similar in both sets of guidelines. And, as mentioned before, these EU and OECD guidelines are merely recommendations that are not legally binding. As far as the EU is concerned, at some point in the future, it may push through actual legislation that is based on these principles. The US has already announced it will adhere to the OECD recommendations.

 

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Lawyers and Tech Competency

Lawyers and technology often have a strenuous relationship, with many lawyers displaying a distinct reluctance to familiarizing themselves with new technologies. Still, tech competency not only provides a competitive edge, but, by now, for most lawyers it also has become an ethical requirement.

In the US, e.g., the American Bar Association’s House of Delegates formally approved a change to the Model Rules of Professional Conduct in August 2012. The new text makes it clear that lawyers have a duty to be competent not only in the law and its practice, but also in technology. Following this change, a lack in tech competency could lead to disciplinary action for misconduct.

The new text of Comment 8 to Model Rule 1.1, which pertains to competence, now states (emphasis added):

To maintain the requisite knowledge and skill, a lawyer should keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology, engage in continuing study and education and comply with all continuing legal education requirements to which the lawyer is subject.

The rule requires lawyers to keep up with the wide range of technology that can be used in the delivery of their services. This means they must stay abreast of the potential risks and benefits associated with any technology they use. It applies, e.g., to Word processing software, email services, security, including safeguarding confidential information, and practice management tools. In some cases, it may even apply to e-discovery or metadata analysis. Casey Flaherty gives the example that a lawyer should probably know how to convert document to PDF, or at least know how to create a document that is completely ready to be converted. In another example, he mentions that a lawyer who is working on a contract with numbered clauses and delegates it to another lawyer should know how to use automatic numbering and cross-referencing.

The competence clause adopted by the American Bar Association is a model rule, which means it must be adopted in a state for it to apply there.  By now, 26 States have done so, and impose an ethical duty of legal tech competence.

As a model rule, each state can implement the rule as it sees fit. In Florida, e.g., this implies, as of 1 January 2017, that all lawyers as a part of their Continuing Legal Education, are required to spend a minimum of three hours over three years in an approved technology program. California, on the other hand, requires lawyers to have knowledge of e-discovery. Indeed, in an age when any court case can involve electronic evidence, every Californian attorney who steps foot in a courtroom has a basic duty of competence with regard to e-discovery.

The rule does not require lawyers to become a technology experts, as they can use the assistance of advisors who have the necessary knowledge. Florida’s competence rule, e.g., states that “… competent representation may involve a lawyer’s association with, or retention of, a non-lawyer advisor with established technological competence in the relevant field.”

Coming back to the example with regard to California and e-discovery, it means that a lawyer in California could face disciplinary action for not properly handling the e-discovery aspects of a case. Robert Ambrogi, in Above the Law, puts it as follows:

That is the key: You need to know enough about e-discovery to assess your own capability to handle the issues that may arise and, if you lack sufficient capability, you can effectively “contract out” your competence to someone else. That someone else could be another attorney in your firm, an outside attorney, a vendor or even your client, the opinion says, provided the person has the necessary expertise. (You cannot, however, contract out your duty to supervise the case and protect your client’s confidentiality.)

By now, two courts have already confirmed that tech competency is required for lawyers. One judge stated that “Professed technological incompetence is not an excuse for discovery misconduct.”

Because of the growing demand for tech-savvy lawyers, several Law School Deans are pushing to add tech to the curriculum. They generally agree that “law schools are a bit remiss in not offering more technology-based training to law students and that they should include legal technology training in the current law school curriculum. The roundtable concluded with the collective position that all law schools in the U.S. owe it to their student bodies to introduce technology-oriented topics into the curriculum in some form or fashion.”

 

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