AI helps with modern slavery due diligence by consolidating scattered supplier data, scoring inherent risk by sector, country and product, and triaging supplier questionnaires so people focus on the suppliers that warrant attention. It cannot tell you whether a supplier uses forced labour. Under Australia's Modern Slavery Act 2018 and its Anti-Slavery Commissioner, people remain accountable for findings, remediation and every statement.
Key takeaways
- Australia's first Anti-Slavery Commissioner began a five-year term on 2 December 2024.
- The Government has agreed in principle to civil penalties, and the mandatory reporting threshold remains $100 million consolidated revenue.
- Organisations below the threshold are still asked to complete modern slavery questionnaires by larger customers.
- Most risk sits two to four tiers down, which makes supply chain mapping a data problem first.
- AI risk scores direct attention. They never prove a supplier is clean.
What is changing in Australian modern slavery reporting?
For its first six years the Modern Slavery Act 2018 was a disclosure regime with little enforcement. Reporting entities lodged an annual statement on a public register, and little happened if it was thin, late or recycled. That is changing:
- Anti-Slavery Commissioner. Appointed under the Modern Slavery Amendment (Australian Anti-Slavery Commissioner) Act 2024, with a five-year term from 2 December 2024.
- Penalties. After the 2024 statutory review, the Government agreed in principle to civil penalties for failing to report, providing false information or ignoring remediation requests, and consulted on design through 2025.
- Threshold. Mandatory reporting stays at $100 million consolidated revenue. A recommendation to lower it to $50 million was not adopted.
- High-risk focus. Reform proposals include giving the Commissioner power to identify high-risk regions, products or supply chains.
What does modern slavery due diligence now require?
The direction is from description to evidence. The original regime asked entities to describe risks and responses. The reforms push toward proof that you looked, found risk where it exists and acted on it.
| Original regime | Direction of reform | Impact | |
|---|---|---|---|
| Consequence for a weak statement | Reputational only | Penalties agreed in principle | Enforceable |
| Oversight | Register, limited follow-up | Dedicated Anti-Slavery Commissioner | Active |
| Expectation | Describe your risks | Evidence real due diligence | Higher bar |
| High-risk focus | Self-defined | Proposed Commissioner focus on high-risk areas | Directed |
| Who is asked | $100m+ entities | Their suppliers too, by contract | Wider reach |
The last row catches suppliers by surprise. If you supply a bank, retailer, miner or government agency, their obligations become your questionnaire. The threshold defines who reports. It does not define who gets asked.
Why is supply chain due diligence a data problem?
The risk usually sits two, three or four tiers down: in cotton, cobalt, seafood, electronics components or labour hire feeding your direct suppliers. A tier-one supplier in Melbourne can look immaculate while sourcing from a high-risk region you have never heard of.
Building the picture means combining supplier lists, spend data, country of origin, product categories and risk indicators, then keeping it current. In spreadsheets it is slow, stale within a quarter and hard to audit. It is the same multi-source data problem covered in supply chain visibility and disruption prediction.
- ConsolidateMerge supplier, spend and category data from finance and procurement
- EnrichAdd country, sector and product risk indicators from published sources
- ScoreRank suppliers by inherent modern slavery risk
- QuestionGenerate targeted questionnaires for high-risk suppliers
- Human reviewAnalysts verify scores and decide remediation
No model can identify which supplier uses forced labour. AI can turn a messy pile of procurement data into a ranked, current, auditable risk map so limited human effort goes where it is warranted.
Where does AI help with modern slavery due diligence?
1. Consolidating supplier data. Few organisations have a clean supplier master. Spend sits in the finance system, contracts in a drive, onboarding forms in email. AI can match entities across inconsistent names and codes into one view, as in supplier onboarding automation.
2. Risk scoring by category and geography. Combining sector, country and product category with published indicators, such as the Walk Free Global Slavery Index and government guidance, gives an inherent-risk score applied consistently across the whole supplier base.
3. Questionnaire triage. Sending every supplier a long survey buries the signal. AI can tailor questionnaires by risk tier and read responses at scale, flagging inconsistent or evasive answers for follow-up.
4. Statement drafting support. For reporting entities, AI can assemble first drafts of factual sections (structure, operations, supply chains, actions taken) from source documents. Your compliance owner verifies and completes it, and a person signs.
- Supplier data consolidationFaster, repeatable
- Inherent-risk scoring across the baseConsistent
- Questionnaire response triageAutomated first pass
- Human judgement on remediationAlways required
AI compresses the data work so people can spend their time on judgement: what a red flag means, how to engage a supplier and what to do about genuine risk.
Where does AI create risk in modern slavery work?
A false clean bill of health is worse than no assessment. These limits are why a person stays accountable.
- Inherent risk is not actual harm. A score shows a supplier operates in a high-risk sector or geography. A low score does not mean a supplier is clean. Use scores to prioritise, never as verdicts.
- Incomplete data produces confident gaps. Suppliers you never captured do not appear as risks. They appear as nothing. Someone must own input completeness.
- Worker data is personal information. Due diligence can involve information about vulnerable workers, handled under the Privacy Act 1988. See our Privacy Act compliance guide for AI.
- Supplier data is commercially sensitive. Consumer AI tools that retain or train on inputs are the failure mode described in how the wrong AI tools leak business data. Keep this work on your own infrastructure with access controls.
- A statement is a public representation. Overstating due diligence with AI-generated language risks misleading conduct exposure under the Australian Consumer Law. Our ACCC consumer law and AI analysis explains why.
What does a defensible AI-assisted due diligence rollout look like?
You need a sequence that produces evidence at each step, so you can show your working to the Commissioner, a customer or a court.
- Weeks 1-3Get the data straightConsolidate suppliers, spend and categories into one auditable view
- Weeks 3-6Score and prioritiseApply published risk indicators; rank suppliers by inherent risk
- Weeks 6-9Engage high-risk suppliersTargeted questionnaires; human review of responses
- Weeks 9-12Document and reportEvidence actions taken; draft the statement if you report
Order matters. Writing the statement before the data and scoring work describes a process you have not run, which is exactly the gap the reforms are designed to expose.
Does your organisation need modern slavery due diligence?
Being below the reporting threshold does not take you out of scope commercially. Use these prompts, then confirm with your advisers.
Which best describes your organisation?
- Consolidated revenue over $100mYou must report; build real due diligence now
- You supply banks, retailers, miners or governmentYou will be asked; prepare to answer credibly
- Sourcing from high-risk sectors or regionsRisk-map even if not required to report
- Domestic, low-risk supply chainLighter touch, but keep the assessment on file
Most mid-sized organisations fall in the middle two rows: no legal duty to lodge, but customers who require evidence that they looked.
How does modern slavery due diligence fit with ESG reporting?
The supply chain data behind a modern slavery risk map also feeds Scope 3 emissions work under climate disclosure, covered in AASB S2 climate reporting for Group 2 entities. Build the supplier foundation once and use it for both.
We built Carbonly.ai, our own ESG and emissions compliance automation product, described in our Carbonly case study. The lesson applies here: the hard part is the trustworthiness of the underlying data and the human accountability around the output. All of it should sit inside an AI governance framework that records who approves, checks and signs before the first supplier is scored.
What should you do this quarter?
- Consolidate supplier data first. Get suppliers, spend and categories into one auditable view.
- Keep the work private. Supplier and worker data stays out of consumer AI tools. Confirm retention and training settings in contracts.
- Score to prioritise. AI risk scores direct attention and never conclude.
- Name the accountable people. Someone owns data completeness, someone owns remediation, someone signs the statement.
- Build once for ESG. The same supplier foundation serves climate and wider supply chain work.
How Solve8 can help
Our consultants and engineers consolidate supplier and spend data, build risk scoring and questionnaire triage, and keep people accountable for every finding, through our process automation and data and reporting services. Everything runs on your infrastructure in Australia. Talk to us about your supply chain data.
Sources: Modern Slavery Act 2018 (Cth); Modern Slavery Amendment (Australian Anti-Slavery Commissioner) Act 2024; Attorney-General's Department material on the Commissioner and the 2024 statutory review response; Walk Free Global Slavery Index. General information, not legal advice.
