Blog for July 2021 Seminar : Ethical Artificial Intelligence

This seminar dealt with the complex issue of ethical artificial intelligence and ontologies. The speaker was Ahren E. Lehnert, a Senior Manager with Synaptica LLC, a provider of ontology, taxonomy and text analytics products for 25 years – http://www.synaptica.com

The central focus of Ahren’s talk was on the relationship between ethics, artificial intelligence and ontologies. Arificial Intelligence (AI) in practice means machine learning leading to content tagging, recommendation engines and terror and crime prevention. It is used in many industries including finance and insurance, job applicants selection, development of autonomous vehicles and artistic creativity. However, we must be careful because there are some outstanding examples of ‘bots behaving badly’. For example, Microsoft’s chatbox, Tay, learned language from interaction with Twitter users. Unfortunately, Twitter ‘trolls’ taught Tay anti-semitic, racist and misogynistic language. Tay was closed down very quickly. Here we are in the territory of ‘ghosts in the machine’ – is that photo really an image of (say) Arnold Schwarzenegger (actor and politician) or is it somebody else who is posing as him or who just happens to look very much like him. More difficult is when you encounter an image of somebody that you know is dead (say) Peter Cushing (actor) whose photo may have been edited into an image that suits a particular project or viewpoint. Are we OK or not OK with these things. It does matter.

Information professionals frequently encounter machine learning – https://en.wikipedia.org/wiki/Machine_learning

Now, however much we may want to go “all in” on machine learning, most companies have not worked out how to “de-silo and clean their data”. Critically, there are five steps to predictive modelling : 1) get data; 2) clean, prepare and manipulate data; 3) train model; 4) test data; 5) improve. We must be sanguine about the results. We will not build a ‘saviour machine’ (!). Machine learning basics include : 1) the need for big data; 2) the need to look for patterns; 3) the need to learn from experience; 4) the need for good examples; 5) the need to take time. We can find good and bad examples of machine learning and we can use the examples of science fiction as portrayed in television and film. For example, ‘Star Trek’ portrays stories depicting humans and aliens serving in Starfleet who have altruistic values and are trying to apply these ideals in difficult situations. Alternatively, ‘Star Wars’ depicts a galaxy containing humans and aliens co-existing with robots. This galaxy is bound together by a mystical power known as ‘The Force’. ‘The Force’ is wielded by two major knightly orders – the Jedi (peacekeepers) and the Sith (aggressors). Conflict is endemic. So bad examples of machine learning (where machine learning fails) arise from insufficient, inaccurate or inconsistent data; finding meaningless patterns; lack of time spent by data scientists on improving machine learning models; the model is a ‘black box’ which users ‘don’t really understand’; unstructured text is difficult.

What is the source of biases which are making their way into machine learning ? Well, people generate context and people have biases to do with : language; ideas; coverage; currency and relevance. Taxonomies are constructed to reflect an organizational viewpoint. They are built from content which can be flawed. The coverage can have topical skews. They can be built by a single taxonomist or a team. The subject matter expertise can be wanting.  Furthermore, Text Analytics is ‘inherently difficult’ : language; techniques; content. Algorithms in machine learning models depend on training data which must be accurate and current with good coverage. Here is a quote from Jean Cocteau – “The course of a river is almost always disapproved of by its source”. Is the answer an ontology ?

What is ethical AI ? What does it mean ? It means being Transparent, Responsible and Accountable. Transparent – Both ML and AI outcomes are explainable.

Responsible – Avoiding the use of biased algorithms or biased data.

Accountable – Taking action ‘to actively curate data, review and test’.

FAST Track Principles – Fairness, Accountability, Sustainability, Transparency.

Whose ethics do we use – the ethics of Captain Kirk from ‘Star Trek’ or the ethics of HAL the computer from ‘2001 A Space Odyssey’. We are back with our earlier ‘Star Trek’ / ‘Star Wars’ conundrum. How will these ethics work out in practice ? How will we reach consensus. How do we define what is ethical and in what context ? Who will write the codes of conduct ? Will it be government ? Will it be business ? Who will enforce the codes of conduct ?

What are the risks given AI in practice ? Poor business outcomes; unintended consequences; mistrust of technology; weaponization of AI technology; political and/or social misinformation; deepfakes; skynet.

Steps towards ethical AI. Steps to success within the organization. Conduct risk assessments; understand social concerns; data sources and data sciences; invest in legal resources; industry and geo-specific regulatory requirements; tap into external technological expertise. There will be goals and challenges to overcome. There should be an ethical AI manifesto or guidelines. An ethical AI manifesto will identify corporate values; align with regulatory requirements; involve the entire organization; communicate the process and the results; nominate a champion. Many existing frameworks of AI Ethics guidelines are vague formulations with no enforcement mechanisms. So,to get started on the AI programme we must clearly define the problem :  what do you want to do ? Why do you want to do it ? What do you expect the outputs to be and what will you do with them ? We must seek to ‘knowledge engineer’ the data to provide a controlled perspective and construct a ‘virtuous content cycle’. We aim for a definitive source for ontologies – authoritative, accurate and objective. Pay particular attention to labelling, quality data and training data. Get the data and create trust in the consuming systems and their resulting analytics and reporting.  Use known metrics. Remember that governance applies to business and technical processes.

 

Rob Rosset 26/07/2021

 

 

 

July 2021 Seminar : Ethical Artificial Intelligence

Summary

What is ethical, or responsible, artificial intelligence (AI) ? In essence, we can identify three concerns/issues : “the moral behaviour of humans as they design, make, use and treat artificially intelligent systems.” “A concern with the behaviour of machines, in machine ethics” – for example, computational ethics. ” “The issue of a possible singularity due to superintelligent AI” – a fascinating glimpse into the future as computers might ‘take over’. Is this still science fiction ?

 https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence#Singularity

This seminar encompassed important topics for Knowledge Management and Information Management  practitioners. Topics included ‘bias’ in the machine, in machine learning, in the content cycle, in the taxonomy, in the text analytics, in the algorithms  and, of course, in the real world. Critically, where do knowledge organization systems fit in and how can practitioners play a role in creating ethical artificial intelligence ?  Should companies begin to develop an AI ethics strategy that is publicly available ?

Speaker

The speaker was Ahren Lehnert  – Senior Manager, Text Analytics Solutions of Synaptica.com and he is based in Oakland, California, USA. https://www.synaptica.com/

Ahren is a graduate of Eastern Michigan University in the Mid-West and a post-graduate of Stony Brook University in New York.

Ahren is a knowledge management professional passionate about knowledge capture, organisation, categorisation and discovery. His main areas of interest are text analytics, search and taxonomy and ontology construction, implementation and governance.

His fifteen years of experience spans many sectors including marketing, health care, Federal and State government agencies, commercial and e-commerce, geospatial, oil and gas, telecom and financial services.

Ahren is always seeking ways to improve the user experience through better functionality and the most ‘painless’ user experience possible based on the state of the industry, best practices and standards.

Time and Venue

Thursday July 22nd at 2:30pm  on a Zoom online meeting.

Slides

Slides available for members in the Members Hub.

Tweets

#netikx111

Blog

There is a blog available here.

Study Suggestions

Ontologies and Ethical AI | Synaptica LLC

Ethics & Bias in the Content Cycle | Synaptica LLC

 

Rob Rosset 27/07/2021

 

 

September 2018 Seminar: Ontologies and domain modelling: a fun (honest!) and friendly introduction

Summary

At this lively meeting Helen Lippell and Silver Oliver introduced ontologies and explained how they could be used. Michael Smethurst and Anya Somerville ran an interactive practical session

Speakers

Helen Lippell has run her own consultancy since 2007, working as a specialist in taxonomy, metadata, ontologies and enterprise search. She loves getting stuck into projects and working with clients to figure out how best to use the messy content and data they have. She has supported organisations such as the BBC, gov.uk, Financial Times, Pearson, and Electronic Arts.
Silver Oliver has worked as an Information Architect for many years. Previously he has worked with the BBC, British Library and government. For the last 10 years he has worked at Data Language, a small consultancy specialising in semantics. His areas of expertise include all areas of information architecture but focuses primarily on the role of domain modelling in delivering design solutions.
Michael Smethurst has worked as an Information Architect for over ten years. Prior to working for the UK Parliament, he worked at the BBC and BBC R&D on a variety of projects, ranging from programmes, iPlayer, news, sport and food. Here he brought together practices from the semantic web and the domain-driven design community. He now works as a data architect for the UK Parliament using the same methods to understand and document parliamentary processes, work flows and data flows.
Anya Somerville is Head of Indexing and Data Management for the House of Commons Library, where she leads a team of information specialists. The team adds subject indexing, links and other metadata to parliamentary business data. It also manages Parliament’s controlled vocabulary. Anya and her team work closely with Michael and Silver on the domain models for parliamentary business. A pdf flyer for this meeting can be downloaded from the link Ontologies and domain modelling

Time and Venue

2pm on 20th September 2018, The British Dental Association, 64 Wimpole Street, London W1G 8YS

Pre Event Information

What exactly is an ontology? How can we use them to better understand our information environments? Helen Lippell and Silver Oliver will be explaining all, providing examples from projects they have worked on, and giving you the chance to build your own ontology and domain model. Helen will give an accessible introduction to what ontologies are, how they are being used in a variety of different applications, how they differ from taxonomies, and how you can combine taxonomies and ontologies in models. This introduction assumes no prior knowledge of ontologies or semantic technologies.
Silver will be explaining how ontologies are used in domain modelling, demystifying some of the terminology, and providing case studies to demonstrate ontologies in practice. There will be the chance to get pens and paper out to produce and develop your own ontology and domain model, with additional help from experienced domain modellers Michael and Anya. You will learn the basic ideas around ontologies and domain modelling and see how ontologies can be used to better understand our information environments. You will begin to learn how to develop and use ontologies

Slides

Slides available.

Tweets

#netikx94

Blog

See our blog report: Ontologies and Domain Modelling

Study Suggestions

Take a look at the Simple Knowledge Organization System Namespace Document

https://www.w3.org/2009/08/skos-reference/skos.html

July 2014 Seminar: Selling Taxonomies to Organisations

Summary

The NetIKX seminar for July addressed the need for a taxonomy and its potential to the organization.

There were two case studies presented. The first from Alice Laird, (ICAEW), faced the business case quandary head on. How did they get hard headed Finance to budget for their taxonomy plans? The winning move here was to show in small scale the value of the work. People in the business realised that the library micro-site was the best place to find things and asked why this was so. The knowledge management team were able to demonstrate how the taxonomy could increase organisational efficiency and so helped prove the case to all website users.

The second case study looked at using a taxonomy to help share data between different organizations in the UK Heritage sector. In a talk called ‘Reclassify the Past’, Phil Carlisle (English Heritage) entertained us giving both the successes and the difficulties. Highlighting what could go wrong was a good way to sell a structured taxonomy project. Search, even with a good search engine is more complex than many people realise and poorly organised metadata can cause problems that ‘Google it!’ may not solve. The session ended with a lively set of discussions.

Both case studies provided valuable tips for running a taxonomy project.

Speakers

Alice Laird is the Taxonomy Project Manager at the Institute of Chartered Accountants in England and Wales (ICAEW). Alice leads a team of two taxonomy full-time taxonomy consultants and one external consultant to create the ICAEW taxonomy and implement it on the ICAEW website.
She liaises with stakeholders in the selection and purchase of suitable taxonomy and auto-classification software. She liaises with stakeholders in the creation and implementation of taxonomy and metadata.

Phil Carlisle is a Data Standards Supervisor at English Heritage and has a wealth of experience (both nationally and internationally) in explaining the need for taxonomies and developing them for the historic environment community.

Time and Venue

2pm on 3rd July 2014, The British Dental Association, 64 Wimpole Street, London W1G 8YS

Slides

No slides available for this presentation

Tweets

#netikx64

Blog

See our blog report: Selling Taxonomies to organisations, Thursday July 3 2014