Inclusive Leadership

What it was

A half day’s training in London on 20 November 2018, attended as part of the Future Leaders Scheme.

What I learned

  • There are broadly three components of inclusive leadership:
  • Culture
  • Relationships
  • Decision making style

Inclusive culture

  • Imagine a time you felt alone in a crowd. How did you feel/think? How did thismake you behave? How might others have interpreted this?
  • Psychological safety: a shared belief that the team is a safe environment to put oneself at risk
  • Psychologist standing: a sense of entitlement to speak up and act
  • Servant behaviour: collective goals and team working for one another
  • Components of trust: cognitive (are they technically capable), affective (do Iget on with them), transactional (do what they say they will do)

Culture tips:

  • Actively encourage everyone to contribute
  • Listen to different views and challenge
  • Value others expertise and experience
  • Create a sense on entitlement to speak up

Inclusive Relationships

  • Building team cohesion:
    • Creating a shared team identity
    • Avoiding fault lines
    • Avoiding favourites
  • Investing time:
    • Get to know people as individuals
    • Increase contact with people from different backgrounds
    • Mentoring people from under represented groups
  • Networks:
    • Diversity of your network
    • Developing their networks

Relationship tips:

  • Conduct a network analysis – how inclusive are you?
  • Work as a team, not sub groups
  • Challenge yourself, don’t go to the usual suspects
  • Invest time
  • Mentor someone different

Inclusive decision-making

  • Openness versus perception of risk
  • Flexibility
  • Avoid gut instinct
  • Awareness of bias
  • Bias thrives under these decision making conditions: Pressure, high cognitive load, need to reach closure, overall impressions, tiredness
  • Micro-messaging: brief verbal and non verbal interactions that make people feel under valued, undermined and excluded
  • Negative micro- behaviours: interrupting, assumptions / benevolent attitudes, limited eye contact, ignoring contributions
  • Micro- affirmations: Non verbal: eyes, body language, acknowledgment, time and attention; Verbal: involving, encouraging; Recalling: remembering (contribution)

Decision making tips:

  • Understand your biases
  • Stand back and look at how decisions are being taken
  • Set the right conditions
  • Be aware of micro behaviours
  • Listen to diverse points of view

We completed a quick inclusive leadership assessment. Actions to address these weakest areas are captured below.

  • My strongest areas were:
    • Psychological safety
    • Openness
    • Flexibility
  • My weakest areas were:
    • Investing time
    • Diverse networks
    • Psychological standing

What I will aim to do differently as a result:

  • Actions to create a more inclusive culture:
    • Allow time for people to speak up, and not just at the end
    • Ask people to say what they want out of the meeting, then cover that
    • Make more time with team leaders to discuss things, not just updates
  • Actions to create more inclusive relationships:
    • Spend more time with my teams at other sites, don’t just go for a meeting and leave
    • Involve a wider set of people in planning and senior team meetings
    • Become a mentor e.g. to a staff member from a minority group
    • Make appointments with colleagues outside my area and increase network contact
  • Actions to create more inclusive decisions:
    • Have one to ones with team members other than team leads. Some people may not feel able to speak up in a group
    • Try the Harvard bias ( implicit association) test

Business Ethics and Artificial Intelligence 2018

What it was:

CIPR written briefing studied as part of Continuous Professional Development, 27 February 2018:

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.