# AI in HR and recruitment: fair, lawful and open to challenge

*When a tool is really making the decision, what candidates must be told, and how to keep discrimination out.*

## Production summary

- Modules to record: 2
- Total script: 832 words, about 6 minutes of finished audio
- Voices: Emma (host) and George (practice educator)
- Level: HR staff, recruiters, hiring managers, registered managers and small business owners

## Accreditation wording that must appear in the description

- **The CPD Certification Service** (planned): Application scheduled.
- **Information Commissioner's Office: automated decision making** (aligned): Written against the ICO's report on automated decision making in recruitment and its draft guidance of 31 March 2026, which was published for consultation and may change. Our own mapping, with no endorsement from the ICO implied.
- **Equality Act 2010** (aligned): The discrimination material is written against the Act. Our own summary, which is not legal advice.

> Do not upgrade any of these words in a description or a thumbnail. Aligned is not accredited, and planned is not approved.


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## When the tool is really deciding, and what candidates must be told

**Runtime** about 3 minutes. **Words** 387. **Starts at** 00:00 in the full course recording.

### Learning outcomes to state on camera

- Describe where AI is used in recruitment
- Tell a solely automated decision from meaningful human involvement
- State the four safeguards for solely automated decisions
- Explain what candidates must be told
- Explain when a data protection impact assessment is needed

### Script


`[CUE 1]` *A recruitment funnel with AI at each stage: advert, sift, video score, ranking*

**EMMA**  [00:00]
George, our applicant system scores every CV and gives me a shortlist. The supplier calls it decision support. Am I fine?

**GEORGE**  [00:08]
Depends what you do with the shortlist. Let me tell you what the Information Commissioner's Office found. On 31 March 2026 they published a report on automated decision making in recruitment, after working with more than thirty employers.

**EMMA**  [00:23]
And?

**GEORGE**  [00:24]
Employers underestimated when tools were making the decision. Many tools described as supporting a human showed no meaningful human involvement in practice.


`[CUE 2]` *A report dated 31 March 2026 with its main finding: no meaningful human involvement*

**EMMA**  [00:32]
I do look at the shortlist.

**GEORGE**  [00:35]
Do you ever change it? Do you ever pull someone back in that it rejected?

**EMMA**  [00:41]
...No. I've never seen the ones it rejected.

**GEORGE**  [00:44]
Then for those candidates, the tool decided. If nobody ever overrules it, it's deciding. Count how often a reviewer changes its outcome. A figure of zero tells you the answer.


`[CUE 3]` *A reviewer with training, information, time and authority, beside a rubber stamp crossed out*

**EMMA**  [00:56]
What would proper human involvement look like?

**GEORGE**  [00:59]
The ICO's draft guidance says active, not tokenistic. The reviewer has to be trained to understand the system's logic, outputs, limitations and risks. And they need the information, the time and the authority to reach a different outcome.

**EMMA**  [01:14]
Our IT lead configured it. Doesn't that count?

**GEORGE**  [01:17]
No. The guidance says someone who designed or built the system isn't meaningful human involvement. That happened before any real decision was made.


`[CUE 4]` *Four safeguards as four cards: told, representations, human intervention, contest*

**EMMA**  [01:26]
So is a fully automated rejection illegal?

**GEORGE**  [01:29]
Not necessarily, since 5 February 2026. The law was amended. A solely automated decision with a significant effect is permitted on ordinary personal data, with safeguards. And rejecting a job application is a significant effect.

**EMMA**  [01:43]
Which safeguards?

**GEORGE**  [01:44]
Four. The person is told about the decision. They can make representations. They can obtain human intervention. And they can contest it.


`[CUE 5]` *A candidate notice in plain words with a route to a human review*

**EMMA**  [01:53]
And if the tool uses health or ethnicity?

**GEORGE**  [01:56]
Special category data. Then solely automated decisions are generally prohibited, unless there's explicit consent or a narrow legal condition.

**EMMA**  [02:03]
What do I have to tell candidates?

**GEORGE**  [02:06]
Clearly, that automated decision making is used and how it works. Not a technical essay. And how to ask for a human review. Plus a data protection impact assessment where the risk is high, kept up to date.

**EMMA**  [02:21]
Is that guidance final?

**GEORGE**  [02:23]
No. It went out for consultation, which closed on 29 May 2026, so it may change. The four safeguards are in the legislation and they're in force now.

### Sources for the on screen credit

- UK ICO consults on draft automated decision making guidance and sets expectations for ADM in recruitment (April 2026), Covington & Burling, Global Policy Watch
- Automated decision making and profiling, Information Commissioner's Office
- Data (Use and Access) Act 2025, section 80, legislation.gov.uk
- AI tools in recruitment: audit outcomes report (November 2024), Information Commissioner's Office

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## Keeping discrimination out, and what to ask a supplier

**Runtime** about 3 minutes. **Words** 445. **Starts at** 02:34 in the full course recording.

### Learning outcomes to state on camera

- Explain how the Equality Act applies to a tool's outcomes
- Describe how a tool can discriminate without being told a protected characteristic
- Monitor outcomes for unfairness
- Make reasonable adjustments where a tool disadvantages a disabled candidate
- Ask a supplier the questions that matter, and say what the EU AI Act adds

### Script


`[CUE 1]` *Nine protected characteristics and a scoring model labelled a criterion*

**EMMA**  [02:34]
George, our supplier says the tool is unbiased because it never sees sex or ethnicity. Is that enough?

**GEORGE**  [02:41]
No, and it's the most common misunderstanding. Start with the law. The Equality Act protects nine characteristics, and it doesn't care whether a decision was made by a person or by software.

**EMMA**  [02:54]
So what's the risk with a tool?

**GEORGE**  [02:57]
Indirect discrimination. A criterion that looks neutral, applied to everyone, which puts people with a protected characteristic at a particular disadvantage. Unlawful unless you can justify it. And a scoring model is a criterion.


`[CUE 2]` *A CV with the name removed and three details still pointing to who the person is: a gap, a postcode, a year*

**EMMA**  [03:11]
But it can't see the characteristic.

**GEORGE**  [03:13]
It doesn't need to. Other details stand in. A gap in employment can stand in for maternity or illness. A postcode for ethnicity. A graduation year for age.

**EMMA**  [03:24]
That's uncomfortable.

**GEORGE**  [03:25]
It gets more so. A tool trained on your past hiring learns your past preferences. If previous hires were mostly of one kind, it favours that kind, and presents it as a score.


`[CUE 3]` *A funnel showing who applies and who passes each automated stage, with a gap marked at one stage*

**EMMA**  [03:38]
So how would I know?

**GEORGE**  [03:40]
Look. Compare who applies with who's passed through at each automated stage, by the characteristics you lawfully collect for monitoring. A large unexplained gap at one stage tells you where to look.

**EMMA**  [03:53]
Doesn't collecting that data make it worse?

**GEORGE**  [03:56]
Not if you keep it separate. The people and the tool making decisions never see it. Do it before go live, using past applications, and regularly afterwards.


`[CUE 4]` *A candidate asking for an adjustment before a timed test and a video interview, and a human route*

**EMMA**  [04:07]
Anything simpler?

**GEORGE**  [04:07]
Yes. Every so often have a person review a sample of the candidates the tool rejected. It's the cheapest test there is.

**EMMA**  [04:16]
What about disabled candidates?

**GEORGE**  [04:18]
You must make reasonable adjustments. And automated stages create new disadvantages. A timed online test for someone with dyslexia. A video interview scored on speech or eye contact, for someone who's deaf, who stammers, or who's autistic.


`[CUE 5]` *Six questions for a supplier, and a calendar marking 2 December 2027*

**EMMA**  [04:33]
So I need a way round the tool.

**GEORGE**  [04:36]
A way to ask for an adjustment before the automated stage, clearly offered, and a human route. A process with no alternative can't meet the duty.

**EMMA**  [04:46]
And if it goes wrong, is it the supplier's problem?

**GEORGE**  [04:50]
Yours. The employer is liable for discrimination in its recruitment, whoever built the tool. The software chose is not a defence in a tribunal.

**EMMA**  [05:00]
What do I ask the supplier, then?

**GEORGE**  [05:03]
What it does at each stage and what data it uses. What it was trained on and how it was tested for bias, with the results. Whether it infers any characteristic. And what you can see about why a candidate got their score.

**EMMA**  [05:20]
We recruit in Ireland too.

**GEORGE**  [05:22]
Then note the EU AI Act. Recruitment AI is high risk there, with duties from 2 December 2027. And emotion recognition at work is already banned.

### Sources for the on screen credit

- Equality Act 2010, legislation.gov.uk
- AI tools in recruitment: audit outcomes report (November 2024), Information Commissioner's Office
- Responsible AI in recruitment (March 2024), Department for Science, Innovation and Technology
- Regulation (EU) 2024/1689 (Artificial Intelligence Act), Annex III, EUR-Lex

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