The Ethics of AI – Technologies change, ethics stay the same

The concept of ethics has existed for about as long as humans have been humans. Although nowadays, many people are disinclined to become entangled in discussions of ethics, whenever a new discipline or technology emerges, the question as to what the ethics of that technology are will inevitably be asked. Such is the case with the ethics of AI [1].

The current consensus around the ethics of AI is that we can build on the four basic principles of bioethics. These are beneficence, non-maleficence, autonomy, and justice. There is also a general tenor that we should augment that list with a fifth principle about ‘explicability’ [2]. Realistically, this was probably always an implicit component of the application of bioethics. Nevertheless, just as when a new CEO comes in, they are obliged to change something about the company, so too when a new technology comes along, we feel compelled to try to re-define the multiple thousands of years old discipline of ethics which has guided us thus far.

To be fair, the application of ethics in any new discipline is a topic which should be discussed and re-discussed. Humans have never agreed on a single, clear definition of what ‘ethics’ is - but we have still been generally pretty good at agreeing on what we definitely do not want to happen. Examples of what not to do in terms of ethics of AI include:

  • Not disclosing sensitive information

  • Not creating opaque applications, whereby the users and the creators don’t exactly understand what they do

  • Not using AI to enhance activities we generally consider to be unethical, such as stealing

Nonetheless, the core problem of ethics has always been bridging the gap between ‘knowing’ and ‘doing’. We can agree on the above examples. The general tenet about what is wrong and right is something that humans have an innate sense for – even if many different words and concepts can be used to describe that.

The question is - How do we ensure that the innate ethical ideals we have been following for centuries are implemented in practice for AI?

  • The first thing is to ensure that discussions on ethics are held, even if it is difficult to agree on specific terminology.

  • The second thing is to use those discussions to ask pragmatic questions about the concrete applications of AI, rather than trying to put labels on lofty ideals.

Aristotle would want you to ask yourself – Is this action consistent with what I consider to be virtuous behaviour? Use whatever labels which come to mind when you ask yourself that question (honest, integral, just, whatever). Other versions of this question include – Would you still do this action if you had to explain it to your mother or your daughter tomorrow? Would you do it if it were on the front page of tomorrow’s newspaper?

Immanuel Kant, the philosopher who defined deontological (read: rules-based) ethics, would want you to ask – Is this action universalizable? That is, if everyone in the world decided to do this action tomorrow, would that be logically possible? Taking an example from the finance world - momentum trading, it would actually fail this test. Momentum trading lives off the assumption that other market participants have identified fundamental information and are trading on that basis. If everyone only conducted momentum trading, so noone is actually conducting fundamental research, this assumption would not hold. Thus, momentum trading is unethical. Obviously, no one is physically harmed by momentum trading, but financial markets are more prone to bubbles (boom and bust cycles) because of it. There is a general consensus that bubbles are bad because they mislead the productivity of the real economy. This question about the universalizability of an action is likely to be helpful in many AI applications. Often, people will not be directly harmed by AI - but if you have a general feeling of unease about a certain use case, this may be your problem.

Utilitarian ethicists would ask - Does the sum of the benefit of the action outweigh the sum of the negative consequences of the action? This utilitarian idea underpins modern economic theory (yes, economic theory is a practical derivative of ethical theory) and it works quite well in an economic context. Monetary gains and losses can be neatly summed and negated. It becomes more challenging when the benefits and drawbacks leave the economic domain and enter e.g. the social and environmental domain. Still, the main thing is to discuss the list of pros and cons and take a decision you feel comfortable with, on balance.

John Rawls did subsequently invent what is known as contractualism, a school of ethics which focuses on the idea of the social contract and asks – Does conducting this action generate the greatest possible benefit for the person in society who is the worst off? This idea recognises that justice can’t mean that everyone is entitled to the exact same life circumstances. Instead, we have to somehow ensure that the people worst off in society would still consent to the social contract of that society. In terms of the classic question as to whether an autonomous car should run over the elderly person or the baby, we could say the person who dies is the worst off. Arguably, a person would prefer to be run over as an elderly person than as a baby. It’s a tough call to make – but ethics has always been about making decisions in tough situations.

Further reading & references:

[1] This website collates existing attempts to define ethics of AI: https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/

[2] This publication summarizes the five principles of the ethics of AI and makes recommendations: https://link.springer.com/article/10.1007/s11023-018-9482-5

Blog by:

Dr. Christina Kleinau

Strategy meets design thinking

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Connecting traditional funding models to modern ways of working

Top down strategy setting and budgeting (Command and Control)

Traditional strategy and organisational management approaches are focused on creating a big picture strategy to maximise the allocation and impact of a finite set resources (capital, people, time etc.). Top down, centralised planning, with well communicated plans to be executed through rigid hierarchical structures.

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Centralised strategy & planning approaches are great for breaking down and coordinating work efforts across large organisations. It suits well when delivering outcomes that require little customer input, but is weak when there is limited information or variability in user preferences. (e.g. Employees don’t really have a choice when it comes to what systems they use, customers can walk away.)

Key issues

  • Big gap between planners and users / customers.

  • Budgets go to the most influential executives.

  • Good ideas generated by front-line staff rarely make it up the chain to be prioritised / funded.

  • Periodic strategic planning means long lead time between budget allocation and execution (lacks nimbleness)

  • Divides up budgets and resources based on organisational structures rather than customer outcomes

  • Progress often judged by spend rate rather than earned value (since there is little to show until right at the end of the project).

  • Good at continuous / incremental change but can only do things where the outcome is well known.

  • Competing functional priorities and silos can produce disjointed customer experiences.

The real problem

Imagine the following scenario: “I have a great idea that our customers will love…Great, what’s the cost, timeframe and return?”

In a traditional structured business environment, significant effort would be spent justifying, obtaining organisational buy-in and estimating the potential costs and benefits of any particular initiative…all before there is any real understanding whether the idea would be desirable to customers.

Bridging the human-computer gap

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Enterprises are embracing digital today, are you being left behind?

Accessibility of enterprise data exposes the challenges for value generation

Accessibility of new technologies means significant upsides in both customer experience and productivity, however Data is a major stumbling block

New technologies within the fields of advanced data analytics, cognitive computing / artificial intelligence and visual recognition will create innovative commercial opportunities for firms that can bind those capabilities with their existing strengths. With the increasing availability of cloud applications, these new capabilities are no longer exclusively available to the largest or technically advanced companies.

Against the backdrop of “being digital” businesses are increasingly seeking commercial applications of these technologies in order to enhance their customers’ experience as well as to reduce costs through intelligent automation.

How enterprises are embracing digital

Improving customer experience

  • Help find relevant products / services within companies or in the market.

  • Help manage finances, budget, household bills and find the cheapest utility providers based on predicted usage.

  • Help identify fraud by reconciling my receipts to my credit card statement.

Cost Saving Propositions through automation

  • Data Entry & form processing

  • Matching invoices, financial reconciliation

  • Legal contract review and compliance reporting

Where to from here?

We are now in a world where the crucial barrier to leveraging these new technologies isn’t necessarily the upfront cost or complexity of managing the software / hardware rather the ability to gain mastery of the underlying data that is required to drive these applications. Something you cannot buy off-the-shelf.

Based on where we are today, to achieve aspirations of automating key customer facing and operational tasks, we need a bridge between the digital and dynamic human world.

Everyday new documents and spreadsheets are being created, businesses have different invoice format, every department has a different spreadsheets and databases. In addition, within most organisations today, significant amounts of data still exists in physical form (printers and filing cabinets are still critical pieces of office furniture). Even when documents are in digital form (word documents), they are not always machine readable (e.g. a document scanned as a PDF).

So far most companies have only been able to digitize forms and computerize processing of very repeatable tasks. It takes time to create new screens in applications and database fields, this process is unable to keep up with the pace of business change.

As data / information management and digitization capabilities evolve the degree of automation available in dynamic commercial environments will also increase.

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Getting this right will bring:

  • More agile software applications due to more flexible data structures.

  • The ability to develop deeper customer or organizational insights by tapping into unstructured data and linking it to structured data.

  • More real-time / on demand insights (dynamically tapping into new data sources such as IOT sources).

  • Ultimately lower barriers and incremental costs of future automation.

Consider the example of automating the process of invoice matching for a finance department; instead of defining within an OCR tool which fields must be captured, we simply feed it multiple versions of the an invoice from a certain supplier and it can figure separate out the data fields vs the data itself. Additionally applications can further work out which general ledger account the value of that invoice needs to be entered into based on what’s happened to similar invoices in the past.

In the near term, manual intervention will be required to curate and correct automated actions, however the burden of design and preparation for automation is dramatically lowered through machine learning techniques.

To push the extent of human tasks we can automate, we will need to master a number of new techniques and change a few old ones in the way we manage data.

As data volume grows within organisations in both volume, variety and velocity; using and managing data within organisations will be more like using and managing data on the internet. Don’t seek to sort of control it, think about harnessing what's relevant for you.

This means:

  • Access to organisational data needs to be opened up, data silos inhibit processing of tasks which span multiple business units.

  • We will need more flexible database structures (and physical places to store data) that business users are able to design, use and control.

  • More collaborative ways of managing data (definitions and descriptions) that is the responsibility of business users. (As opposed to centralized classification schemes maintained by technical experts.)

  • We will need the ability for machines to reliably infer meaning from natural language text (i.e. summarizing data, think of automatically adding hashtags)

  • More accurate OCR (optical character recognition) capabilities that includes the ability to translate hand writing to machine readable data.

  • We need to employ AI / machine learning techniques that can automatically classify data and cross reference data between documents and structured data sources.

Hey I'm not reading anything here that I don't already know!

Some of you are thinking that there's nothing new here and that is exactly my point. Most of the software is available today.

We use to have to go out and buy expensive scanning solutions or document management systems, now it's available in the cloud. If there's functionality you need, there's likely an API you can call to obtain it or there will be one available shortly.

The data only the other hand, is created by people. Often inconsistently, with differing purposes and meanings over time.

Over the last 30-40 years, we've only been trying to force people to conform to processes that machines understand. We are now at a time where we can start bringing machines closer to understanding people.

This post represents my personal views. Let me know your thoughts in the comments.

Digital for Australian businesses

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Is your organisation ready to take advantage of the digital economy?

Why do companies need help in adapting to the new digital economy?

There are unprecedented challenges and opportunities with relation to the new digital economy. Customer preferences and behaviours are changing and data is growing. Companies will need to be able to master insights and connectivity and insights with their customers to bring relevant offerings with speed and scale in order to succeed.

Problems facing mid-sized firms wedged between large enterprise and small businesses

  • They are not as nimble as small business that can easily re-invent, pivot, hire/fire people and are subject to more onerous labour laws.

  • They require general management practices and suffer from all traditional organisational challenges (silos, red-tape)

  • They do not have as deep pockets as large corporates so therefore find it harder to attract and sustain diverse pools of specialist talent. (you can’t hire a bunch of people then fire them when you pivot your offering)

  • They can’t afford high end advisory services of large corporates.

How we can help

  • Bring management and cultural mindsets and organisational practices adopted by the most innovative companies in the world.

  • Tailor a set of lean start-up principles, processes and approach that can be gradually scaled out across the organisation (e.g. islands of freedom).

  • Adopt human centred design principles and tools when developing customer facing offerings.

  • Leverage aspects of traditional strategy development and management principles so that these new practices can co-exist and mature within the organisation. (i.e. keeping the CFO happy).

Our value proposition

  • We will upskill your leadership team and get them into the required mindset for digital change by taking them through an interactive process that facilitates them to build a digital vision and roadmap that they own. This ensures any digital initiative is supported by the broader organisation.

  • Through our workshops, we develop actionable plans as well as organisational roles and responsibilities that lead into your first digital / innovation project.

  • We utilise lean start-up and human centred design methods our methods to help customers products and services to market, however we develop tailored processes for our clients based on their current organisational structure and level of maturity.

  • We have a deep understanding of modern capabilities such as UX/design, Cloud, Big Data, advanced analytics, machine learning and enterprise collaboration tools.

  • We are experienced in large Enterprise strategy planning and project portfolio management.

Our Approach

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