Business Ethics across Generations

What it was:

A report on trends in attitudes to ethics in business, reviewed as part of my PR CPD.
Source: http://bit.ly/1RJl74H

What I learned:

Ageing  populations  and the new generation entering  the workplace are creating opportunities and challenges for employers in embedding ethics.

Understanding different characteristics of these generations is fundamental to building a culture founded on ethical values.

Four generations can be (loosely) identified:

  • Traditionalists (born between 1922 and 1945)
  • Baby Boomers (born between 1946 and 1964)
  • Generation X (born between 1965 and 1982)
  • Generation Y or Millennials (born between 1983 and 2004).

Millennials account for 25% of the workforce in the US and it is predicted that by 2020, they will form 50% of the global workforce

Boomers and Traditionalists seem to be less prepared than other age groups, as they developed professionally before such a function became commonplace. However, the underlying attitudes of these two generations seem to be less accepting of unethical behaviour.

Millennials want worthwhile work – a majority want to work for a company that makes a positive impact, half prefer purposeful work to a high salary, and 53% would work harder if they felt they were making a difference to others

Organisations should seek to leverage the different generations’ strengths, to create a working environment that values differences and bridges potential generational gaps

A strong ethics culture can motivate employees to do the right thing and increase employee engagement

Use of ethics ambassadors across the divide: Potentially,  Millennials can be effective ethics ambassadors as they are natural networkers and familiar with new technologies, but at the same time older employees may have a more established reputation for integrity.

Use of metrics and bench-marking to segment the workforce can be useful to understand the employee’s expectations from their job at different stages of their career.

What I will aim to do differently as a result:

Try to reflect likely communication styles of different generations in internal communications activities.

Remember different motivations of staff of different generations.

Consider use of “generational ambassadors” in internal campaigns.

Try to ensure that senior staff are aware of these differences when they communicate (junior staff likely to be a different generation from them!)

Be careful about following this analysis too slavishly – cannot discriminate against any one generation (Equalities Act) e.g. by assuming one generation is susceptible to acting unethically. 

Also a good idea to take such inter-generational analyses with a pinch of salt! 

 

Business Ethics and Artificial Intelligence 2018

What it was:

CIPR written briefing studied as part of Continuous Professional Development, 27 February 2018:
Source: https://www.ibe.org.uk/userassets/briefings/ibe_briefing_58_business_ethics_and_artificial_intelligence.pdf

What I learned:

AI is relevant to Business Ethics – for example, how do you ensure your organisation’s values are being applied if decisions are being made algorithmically?

Potential risks of AI:

  • Ethics risk
  • Workforce risk (loss of jobs /skills)
  • Technology risk (cyber-attacks)
  • Algorithmic risk (biased decisions)
  • Legal risk (privacy / GDPR)

AIs may be accurate but nonetheless reflect human biases.

Open-sourcing may be important for openness and trust in AI systems – this could be especially true in Govt where trust is critical and keep source closed is less important.

“Explainability” is key to AI trust and to working alongside an AI partner.

AI work and contracts should specify responsibilities carefully – AIs cannot be held responsible for their behaviour!

Some practical steps organisations can take:

  • Meta-decision-making to ensure AI systems act in line with organisational ethical values.
  • Make sure third party algorithms adhere to ethical standards.
  • Establish a multi-disciplinary Ethics Research Unit.
  • Introduce ‘ethics tests’ for AI machines, where they are presented with an ethical dilemma.
  • Ensure staff have access to relevant training courses and communications re: ethical use of AI

What I will aim to do differently as a result:

In future I will:

  • Think about ethical and legal and other risks of AI projects at the design stage
  • Continue my learning and investigation of AI as applied to my organisation’s business
  • Consider whether ethics, compliance and legal teams should be engaged in AI projects.

Attitudes of the British Public to Business Ethics 2017

What it was:

Review of an article on business ethics current trends as part of my CIPR CPD. Source: https://www.ibe.org.uk/userassets/briefings/ibe_survey_attitudes_of_the_british_public_to_business_ethics_2017.pdf

What I learned:

Trust in business ethics has recovered very slightly (48% to 52%) since last year

The key issues of concern are:

  • Corporate tax avoidance
  • Executive pay
  • Exploitative labour practices
  • Work/life balance

As an observation – these top 4 issues are (arguably) linked, focusing around perceived unfairness between senior executives and staff.

The report notes that the relative recovery in attitudes to business ethics could be driven by falls in attitudes to ethics in other sectors (presumably Government)

Among the lower-level issues of ethical concern, Human Rights has dropped off as a concern. Meanwhile Data/Privacy has grown as an issue of concern.

Millennials appear less concerned about data privacy, perhaps counter-intuitively?

What I will aim to do differently as a result:

  • Recognise that corporate reputation concerns are affected by current events and press coverage, just as in the public sector
  • Appreciate that the public’s issues of ethical concern evolve over time but show reasonable consistency from year to year – i.e. they are not overly driven by current events
  • Recognise that underlying themes (e.g. perceived unfairness between senior executives and staff) can manifest across numerous ethical themes
  • Remember that different generations of staff may react differently to ethical issues.

CIPR Webinar – Change Communication

What it was:

CIPR Webinar on Change Communication presented by Alison Arnot.

Source: http://bit.ly/1El4UJS

What I learned:

Overall points from the webinar:

  • Change is a normal part of business process so a successful business must be able to do it sustainably and repeatedly.
  • People naturally wary of change. It is experienced differently by different people
  • The change curve is superficially similar to the grief curve!
  • Communicators job is to help people move through the change process
  • Those closely involved in the change process typically have a different view as they have more insight and empowerment in respect of the change.
  • Ultimate goal is to get people engaged and motivated to help deliver the future
  • Measurement / evaluation is key. You can’t influence what you don’t understand
  • Measure effectiveness, understanding, feelings, engagement and behaviour
  • Can use e.g. Bench-marking and demonstration of objective evidence of actual changes in behaviour

Detailed points:

Change communication strategy needs to include…

  • Why communicate? Who communicates with whom and about what?
  • What channels? When?
  • AND Consequences and measurement?

Analysing stakeholders needs to ask…

  • What is it like working with us? Who influences them? Who do they influence
  • What is our place in their aspirations? What is their view of our future? How can we help each other?
  • What is their motivation/agenda?

Stakeholder mapping – “Power vs. Interest”

  • High power low interest: Keep satisfied – A threat if they don’t understand
  • High power high interest: Engage – Can make or break your programme
  • Low power low interest: Monitor – Inform but don’t overload
  • Low power high interest: Inform – An advocate and ear to the ground

Communication content needs to address….

  1. STRATEGIC Information
  • Vision, values and direction
  • Rationale and benefits of the change
  • Comms outcome: Sense of purpose
  1. CORPORATE Information
  • How we are progressing?
  • Is the change helping?
  • What success is being had?
  • Comms outcome:  Sense of progress
  1. OPERATIONAL Information
  • What we need to start, stop and continue doing to make the change a success
  • Comms outcome: Sense of control

Messaging needs to address the three following needs…

  • Personal needs – a realistic (not evangelical) appraisal of the situation and what it means for me
  • Operational needs – where we are, where we need to be, what we need to do
  • Strategic needs – the big picture

What I will aim to do differently as a result:

I will conduct a stakeholder mapping exercise (identifying them all, then mapping power vs interest) for my digital transformation programme.

I’ll tailor our programme comms to ensure it covers the Strategic / Corporate / Operational content-types outlined in this briefing.

I’ll ensure that our messaging is better at addressing the three “needs” i.e. Personal / Operational / Strategic.

I will design a system of bench-marking for the programme – potentially a maturity model. For example %age of people that agree with each of the 10 statements in the digital vision.

Civil Service Alumni: Introduction to Artificial Intelligence

What it was:

Talks by subject matter experts on AI hosted by the Civil Service Alumni, at the Royal College of Defence Studies, London on 18 December 2017

What I learned:

AI is permeating business:

  • No single agreed definition of “AI”
  • ‘Narrow’ AI does specific tasks, as opposed to ‘General’ AI which can solve new problems
  • AI is already in use in hundreds of Google products
  • AI is increasingly in the plans of the forward-looking global industrials
  • Huge landscape of firms involved in different sectors

Deep Neural Networks:

  • The workhorse of modern AI is the deep neural network, where each layer of neurons is connected to the prior layer
  • Supervised versus unsupervised learning
  • AI performance is better than humans in many narrow case, using supervised data and deep neural networks

Conditions for success:

  • Many AI applications can be characterised and understood as input X leading to output Y to solve problem Z.  For example, medical scan data leading to disease symptom recognition to solve diagnosis.
  • A precondition for the successful deployment of AI is a clear definition of the system or process, and an understanding of what AI can do
  • A social technical approach, including human impact, is typically desirable
  • AI typically requires widespread digitisation of the system
  • Data requirements: Common definitions, high quality, accessible, sufficient size
  • Domain expertise is key

Impact on Government?

  • A viable, shared business model for both public and private sector is needed
  • Key challenges for government in terms of  capability, ethics, regulation, getting on front foot in terms of impact, and in nurturing the wider AI economy
  • There is a significant discussion happening now around ethics, bias, regulation

What I will aim to do differently as a result:

  • Think about ethics and regulation of AI
  • Think about impact of AI on Government, including potential oportunities
  • Continue my learning theme on AI!