Capitalising bullet point entries

docs/our_work/ai-ethics.md

@@ -24,25 +24,25 @@ This paper proposes that data scientists specifically focus on trying to ensure
 
 Our practical suggestions of how we can work towards embedding these characteristics in working practices include:
 
-* trialling tools and frameworks on live and emerging projects and to share learnings with the wider community. This will eventually constitute a portfolio of real examples that can inform future projects, similar to the use cases features in the [OECD's Catalogue of Tools & Metrics for Trustworthy AI](https://oecd.ai/en/catalogue/tool-use-cases).
-* coordinating a series of interactive workshops to build awareness of ethical risks, and to create and sustain a shared vocabulary to document and help mitigate these risks effectively along each project’s lifecycle.
-* developing standardised resources that can be flexible to different types of data science projects, but ensure a minimum level of consideration and proportionate action.
-* mapping the development processes and involved actors of AI at the NHS and growing a platform to share knowledge and experiences. This will help us to identify how to incorporate ethical considerations into the data science lifecycle.
+* Trialling tools and frameworks on live and emerging projects and to share learnings with the wider community. This will eventually constitute a portfolio of real examples that can inform future projects, similar to the use cases features in the [OECD's Catalogue of Tools & Metrics for Trustworthy AI](https://oecd.ai/en/catalogue/tool-use-cases).
+* Coordinating a series of interactive workshops to build awareness of ethical risks, and to create and sustain a shared vocabulary to document and help mitigate these risks effectively along each project’s lifecycle.
+* Developing standardised resources that can be flexible to different types of data science projects, but ensure a minimum level of consideration and proportionate action.
+* Mapping the development processes and involved actors of AI at the NHS and growing a platform to share knowledge and experiences. This will help us to identify how to incorporate ethical considerations into the data science lifecycle.
 
 ## Outputs
 
 We have:
 
-* developed a [Model Card Template](https://github.com/nhsengland/model-card).
-* coordinated the publication of a record for the [Automatic Moderation of Ratings & Reviews project](./ratings-and-reviews.md) on the government's [Algorithmic Transparency Recording Standard](https://www.gov.uk/algorithmic-transparency-records/nhs-england-nhs-dot-uk-reviews-automoderation-tool).
-* written a (currently internal) White Paper defining the scope of operationalising AI Ethics in NHS England.
+* Developed a [Model Card Template](https://github.com/nhsengland/model-card).
+* Coordinated the publication of a record for the [Automatic Moderation of Ratings & Reviews project](./ratings-and-reviews.md) on the government's [Algorithmic Transparency Recording Standard](https://www.gov.uk/algorithmic-transparency-records/nhs-england-nhs-dot-uk-reviews-automoderation-tool).
+* Written a (currently internal) White Paper defining the scope of operationalising AI Ethics in NHS England.
 
 ## In progress
 
 We are currently exploring:
 
-* how we can use the [Data Hazards project](https://datahazards.com/) to communicate potential harms of our work
-* the development of a generic statement detailing how projects have taken ethical considerations into account
-* supporting our information governance teams on whether additional instruments (such as model cards) can help inform a Data Protection Impact Assessment for AI use cases
+* Using the [Data Hazards project](https://datahazards.com/) to communicate potential harms of our work.
+* Developing a generic statement detailing how projects have taken ethical considerations into account.
+* Supporting our information governance teams on whether additional instruments (such as model cards) can help inform a Data Protection Impact Assessment for AI use cases.
 
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