US dataswitch to UK
Arbitrators, Mediators, and Conciliators
preparing written opinions or decisions regarding cases, using mediation techniques to facilitate communication between disputants and conducting initial meetings with disputants to outline the arbitration process. If that's your week, this page is about your job.
The honest answer
AI is already taking a real slice of the routine work here: setting up appointments for parties to meet for mediation. That is a slice of tasks, not of you.
That slice is not coming back; the core of the job, conducting hearings to obtain information or evidence relative to disposition of claims, stays yours. The tools change hands, the accountability doesn't.
Your week, as this page understands it
Facilitate negotiation and conflict resolution through dialogue. Resolve conflicts outside of the court system by mutual consent of parties involved. The job title says “arbitrators”, “mediators” or “conciliators”: officially one job, several names. The real job is the part underneath: conducting hearings to obtain information or evidence relative to disposition of claims. That is the thing someone has to be right about.
The exposed part of this job is specific, and we won’t pretend it is coming back. But arbitrators, mediators, and conciliators is not one task. It is 20 scored ones, and they are nowhere near equally exposed. The most durable of them, on this evidence, is conducting hearings to obtain information or evidence relative to disposition of claims, and the ledger below shows exactly why.
Where the work sits, by task weight
- shifting to AI
- 23%
- changing shape
- 22%
- staying human
- 55%
These bars are tasks changing hands, not people being counted out. The ledger below shows which.
Whole-job exposure score 39 out of 100 (33–44 allowing for uncertainty): low exposure, across 20 scored tasks. The number is the support for the sentence above it, not a headline about anyone’s future.
How we know this
What is measured: Every published task statement for arbitrators, mediators, and conciliators is rated on five dimensions: can a model produce the output, does the work need a body in a room, does it need a legally accountable person, does it depend on a person being trusted in the moment, and how much data exists. A published formula turns those five ratings into the score; the model never writes the number.
How the bar is built: Each task’s share of the bar is its published importance weight, so a task you do all day counts for more than one you do twice a year.
Release: 2026-q4.1, scores computed 2026-08-05. Read the full method.
Your job, task by task
These are the official task statements for this occupation, in plain English, sorted by what the evidence says is happening to each one. The official wording sits under every line so you can check the rewrite against it.
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- 1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
Shifting to AI
5 tasksTasks today’s tools can already do most of. This is the part we will not soften: where these rows are the bulk of your week, the week changes.
Evaluating information from documents, such as claim applications, birth or death certificates or physician or employer records
This is reading one thing and writing another: information in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Evaluate information from documents, such as claim applications, birth or death certificates, or physician or employer records.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (62–70 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reading claim forms, certificates and employer records to pull out the facts is exactly what document software is good at.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Setting up appointments for parties to meet for mediation
This is reading one thing and writing another: appointments in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Set up appointments for parties to meet for mediation.” (O*NET task statement)
How this row was scored
Exposure score: 85 out of 100 (81–89 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Arranging meeting times between parties is straightforward scheduling that software handles completely.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 1/4 · how much data exists 4/4.
Researching laws, regulations, policies or precedent decisions to prepare for hearings
This is reading one thing and writing another: laws, regulations, policies or precedent decisions in, a record out. That is the shape today's tools are built for.
importance 4 · CoreSource: “Research laws, regulations, policies, or precedent decisions to prepare for hearings.” (O*NET task statement)
How this row was scored
Exposure score: 72 out of 100 (68–76 allowing for uncertainty): high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Legal and policy research over published sources is well suited to software, with the neutral checking the result.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Prepare settlement agreements for disputants to sign
This is reading one thing and writing another: settlement agreements in, a record out. That is the shape today's tools are built for.
importance 3 · CoreSource: “Prepare settlement agreements for disputants to sign.” (O*NET task statement)
How this row was scored
Exposure score: 88 out of 100 (84–92 allowing for uncertainty): very high exposure, high confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Settlement agreements follow standard forms, so software produces a solid first draft for the parties to check.
The five ratings: output a model can produce 4/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 4/4.
Changing shape
4 tasksTasks where the machine takes the producing and a person keeps the checking, the deciding, or the answering-for-it. For most jobs this is the biggest group, and it is where "transformation, not termination" is literally visible.
Preparing written opinions or decisions regarding cases
The software now makes the first pass at written opinions, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Prepare written opinions or decisions regarding cases.” (O*NET task statement)
How this row was scored
Exposure score: 47 out of 100 (40–54 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Software can draft reasoned decisions from the papers, though a judgment must be a judge's own and is signed as such.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Applying relevant laws, regulations, policies or precedents to reach conclusions
The software now makes the first pass at relevant laws, regulations, policies or precedents, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 5 · CoreSource: “Apply relevant laws, regulations, policies, or precedents to reach conclusions.” (O*NET task statement)
How this row was scored
Exposure score: 47 out of 100 (40–54 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Matching facts to law and precedent is well documented work, yet the conclusion legally belongs to the appointed decision-maker.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Authorizing payment of valid claims
The software now makes the first pass at payment of valid claims, but someone has to be answerable for the result, and it cannot be the software. So the job becomes checking and deciding rather than producing.
importance 3 · CoreSource: “Authorize payment of valid claims.” (O*NET task statement)
How this row was scored
Exposure score: 56 out of 100 (49–63 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Checking a claim against the rules and releasing payment is largely mechanical, though someone accountable signs it off.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Staying human
11 tasksTasks that stay with a person, because they happen in the physical world, because the rules need someone accountable, or because the value is that a specific person does them.
Conducting hearings to obtain information or evidence relative to disposition of claims
The rules require a named, qualified person to answer for hearings, and that person cannot be a piece of software.
importance 5 · CoreSource: “Conduct hearings to obtain information or evidence relative to disposition of claims.” (O*NET task statement)
How this row was scored
Exposure score: 11 out of 100 (7–15 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Running a hearing means presiding live over people giving evidence, which the law reserves for the appointed neutral.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 3/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
Ruling on exceptions, motions or admissibility of evidence
The rules require a named, qualified person to answer for exceptions, motions or admissibility of evidence, and that person cannot be a piece of software.
importance 5 · CoreSource: “Rule on exceptions, motions, or admissibility of evidence.” (O*NET task statement)
How this row was scored
Exposure score: 29 out of 100 (25–33 allowing for uncertainty): low exposure, high confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Ruling on motions and what evidence is allowed is legally an act of the person presiding.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 4/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conferring with disputants to clarify issues
The rules require a named, qualified person to answer for disputants, and that person cannot be a piece of software.
importance 4 · CoreSource: “Confer with disputants to clarify issues, identify underlying concerns, and develop an understanding of their respective needs and interests.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Getting people in conflict to open up about what they really need depends on trust built in the room.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Show the other 10 tasks
Conducting studies of appeals procedures to ensure adherence to legal requirements or to facilitate disposition of cases
shifting to AIThis is reading one thing and writing another: studies of appeals procedures in, a record out. That is the shape today's tools are built for.
importance 3 · SupplementalSource: “Conduct studies of appeals procedures to ensure adherence to legal requirements or to facilitate disposition of cases.” (O*NET task statement)
How this row was scored
Exposure score: 66 out of 100 (59–73 allowing for uncertainty): high exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; mistakes that are cheap to catch.
The rating behind it: Reviewing how appeals are handled against legal requirements is a documented analysis software can draft.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 1/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Organizing or delivering public presentations about mediation
changing shapeThe software now makes the first pass at public presentations, but the part that matters is a person saying it and standing behind it. So the job becomes checking and deciding rather than producing.
importance 3 · SupplementalSource: “Organize or deliver public presentations about mediation to organizations, such as community agencies or schools.” (O*NET task statement)
How this row was scored
Exposure score: 53 out of 100 (46–60 allowing for uncertainty): partial exposure, medium confidence.
Why it sits in this group: reading one thing and writing another; the same decision, made over and over; the value is that a specific person does it.
The rating behind it: Talk content and slides are easy to produce, but standing in front of a community group is a person's job.
The five ratings: output a model can produce 3/4 · needs a body in a room 0/4 · needs an accountable person 0/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Recommending acceptance or rejection of compromise settlement offers
staying humanThe rules require a named, qualified person to answer for acceptance, and that person cannot be a piece of software.
importance 4 · CoreSource: “Recommend acceptance or rejection of compromise settlement offers.” (O*NET task statement)
How this row was scored
Exposure score: 37 out of 100 (30–44 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: A settlement figure can be modeled, but recommending acceptance carries professional judgment about this particular dispute.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 2/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Determining extent of liability
staying humanThe rules require a named, qualified person to answer for extent of liability, and that person cannot be a piece of software.
importance 5 · CoreSource: “Determine extent of liability according to evidence, laws, or administrative or judicial precedents.” (O*NET task statement)
How this row was scored
Exposure score: 36 out of 100 (29–43 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Liability rules are documented, but deciding how much someone owes is the appointed decision-maker's legal act.
The five ratings: output a model can produce 2/4 · needs a body in a room 0/4 · needs an accountable person 3/4 · needs to be trusted in the moment 0/4 · how much data exists 3/4.
Conducting initial meetings with disputants to outline the arbitration process
staying humanThe rules require a named, qualified person to answer for initial meetings, and that person cannot be a piece of software.
importance 4 · CoreSource: “Conduct initial meetings with disputants to outline the arbitration process, settle procedural matters, such as fees, or determine details, such as witness numbers or time requirements.” (O*NET task statement)
How this row was scored
Exposure score: 23 out of 100 (16–30 allowing for uncertainty): low exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Process explanations and fee terms are standard, but the first meeting sets the tone between real people.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 2/4 · how much data exists 3/4.
Issuing subpoenas or administering oaths to prepare for formal hearings
staying humanThe rules require a named, qualified person to answer for subpoenas, and that person cannot be a piece of software.
importance 4 · CoreSource: “Issue subpoenas or administer oaths to prepare for formal hearings.” (O*NET task statement)
How this row was scored
Exposure score: 18 out of 100 (11–25 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: the same decision, made over and over; someone qualified has to answer for it.
The rating behind it: Subpoena paperwork is templated, but issuing it and swearing witnesses are formal acts only the presiding person may do.
The five ratings: output a model can produce 2/4 · needs a body in a room 1/4 · needs an accountable person 4/4 · needs to be trusted in the moment 1/4 · how much data exists 3/4.
Interviewing claimants, agents or witnesses to obtain information about disputed issues
staying humanThe rules require a named, qualified person to answer for claimants, agents or witnesses, and that person cannot be a piece of software.
importance 4 · SupplementalSource: “Interview claimants, agents, or witnesses to obtain information about disputed issues.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (3–17 allowing for uncertainty): minimal exposure, medium confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Getting an honest account from a claimant or witness depends on the rapport built during the conversation.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Specializing in the negotiation and resolution of environmental conflicts involving issues
staying humanThe rules require a named, qualified person to answer for the negotiation, and that person cannot be a piece of software.
importance 3 · SupplementalSource: “Specialize in the negotiation and resolution of environmental conflicts involving issues such as natural resource allocation or regional development planning.” (O*NET task statement)
How this row was scored
Exposure score: 10 out of 100 (0–22 allowing for uncertainty): minimal exposure, low confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Settling environmental disputes turns on trust between communities, agencies and developers, built through live negotiation.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 3/4 · how much data exists 2/4.
Using mediation techniques to facilitate communication between disputants
staying humanThe rules require a named, qualified person to answer for mediation techniques, and that person cannot be a piece of software.
importance 4 · CoreSource: “Use mediation techniques to facilitate communication between disputants, to further parties' understanding of different perspectives, and to guide parties toward mutual agreement.” (O*NET task statement)
How this row was scored
Exposure score: 7 out of 100 (3–11 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Mediation works because a trusted human sits between two sides; that relationship is the job.
The five ratings: output a model can produce 1/4 · needs a body in a room 1/4 · needs an accountable person 2/4 · needs to be trusted in the moment 4/4 · how much data exists 2/4.
Participating in court proceedings
staying humanThis work happens in the physical world: court proceedings, in a real place. Software cannot follow it there.
importance 3 · SupplementalSource: “Participate in court proceedings.” (O*NET task statement)
How this row was scored
Exposure score: 3 out of 100 (0–7 allowing for uncertainty): minimal exposure, high confidence.
Why it sits in this group: work that happens in the physical world; someone qualified has to answer for it; the value is that a specific person does it.
The rating behind it: Taking part in court proceedings is a formal role the law gives to a person, in the room.
The five ratings: output a model can produce 0/4 · needs a body in a room 2/4 · needs an accountable person 4/4 · needs to be trusted in the moment 2/4 · how much data exists 2/4.
What this job pays, and how many people do it
- Median pay
- $75,530a year, the middle of the range, so half earn more and half earn less.bls-oews, 2025 · May 2025 estimates (national_M2025_dl.xlsx)
How we know this
Source: bls-oews
Reference period: May 2025 estimates (national_M2025_dl.xlsx)
Rounding: Shown as published.
- People doing this job
- 9,210in the US, 2025.bls-oews · May 2025 estimates (national_M2025_dl.xlsx)
What is deliberately not here: a forecast of how many of these jobs exist in ten years. Where an official projection exists for a market we publish it with its vintage; where it does not, we leave the space empty rather than borrow the other country’s number.
Why this is shifting
The reason is boringly specific. Most of what is shifting here is reading one thing and writing another: written opinions in, a record out. The rows above are exactly that shape: setting up appointments for parties to meet for mediation and evaluating information from documents. What it cannot do is be answerable: hearings need a named person the rules will accept, and software cannot be that person. Which is why this page talks about your tasks changing, not your job ending.
Your move
Over a pint: what I’d tell you if you were my friend
Your week is splitting in two, and which half fills it is the whole question. Setting up appointments for parties to meet for mediation is going; conducting hearings to obtain information or evidence relative to disposition of claims is not.
So, given all that: 23% of this job's task weight sits in rows the software is already learning, 22% in rows that change shape rather than disappear, and 55% in rows it is nowhere near. That is the position, measured across 20 scored tasks. It is not a forecast about you.
The occupation is an average and you are not, so the first job below is finding out where your own week actually sits.
This week: one thing
Print the task ledger above and put your own hours next to each row. Roughly, in pen, in one sitting. Then look at which group your hours actually pile up in. Twenty minutes, this week.
- What you end up holding
- your own week, on one page, sorted into what is shifting and what is not
- How long it takes
- about twenty minutes
If printing it isn’t practical: Read the rows off this page and write the same list on the back of an envelope. Same twenty minutes, same page, and it works just as well said out loud to someone who knows the job. The point is your hours next to the rows, not the paper it is on.
Over the next 90 days
Volunteer toward the durable end, visibly. Over the next ninety days put your hand up for the work in the bottom group (conducting hearings to obtain information or evidence relative to disposition of claims) and let people see you doing it. Not a new project: the same job, with your mix deliberately tilted. The point is that when the rota or the reorganisation comes, the version of you people picture is the one doing the part that stays.
Over the next 12 months
Claim a specialism at the durable end and let the other end go. Over a year, deliberately become the person who handles conducting hearings to obtain information or evidence relative to disposition of claims, and deliberately stop being the first choice for the rows in the top group. That trade costs something, because the exposed work is often the comfortable work. Decide it on purpose rather than by drift. Before you pay for anything, use CareerOneStop - Find local training. It is free, it is the Labor Department's own service, and it is listed below with the rest of the free routes.
The roads out of here, and why I am not sending you down them
I looked at the obvious moves out of this job, and here is what I found.
I checked the 12 nearest US occupations to arbitrators, mediators, and conciliators (nearest by the work that AI is not taking, not by job title), and none of them survived. The closest was administrative law judges, adjudicators, and hearing officers: only about 34% of its durable work is work you already do. And on the numbers you do not need one. This job scores 39/100 here, with only 23% of the task list in the top band, and “prepare written opinions or decisions regarding cases” is not work that hands over cleanly. None of them beats deepening what you already have.
How that was checked: this job was compared against all 830 US occupations in this release on their official task statements, and the 12 nearest were examined one by one. A move that turns on an industry, an employer or a qualification rather than on the work itself will not show up in a check like that. And this release carries no licence register, so anything you are weighing needs that looked up separately.
3 moves I checked and rejected
These are the obvious-looking jumps. They are here with their reasons rather than quietly dropped, because the ones that fail are worth knowing about. It is one less thing to turn over at night.
Administrative Law Judges, Adjudicators, and Hearing Officers
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already rule on exceptions, motions, or admissibility of evidence, and their equivalent is to rule on exceptions, motions, and admissibility of evidence. Across both published task lists that is about 34% of the durable work in that job.
Why I am not recommending it: It is closer than most, and still not close enough: about 34% of that job's durable work is already yours, against the 35% I want to see before I will call something a route.
Judges, Magistrate Judges, and Magistrates
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already rule on exceptions, motions, or admissibility of evidence, and their equivalent is to rule on admissibility of evidence and methods of conducting testimony. Across both published task lists that is about 8% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 8% of the durable side of that job. That is a different job, not a next step. The pay gap is the market pricing a barrier: $153,990 against your $75,530 is 2.04× (OEWS May 2025 (both)), and you would be crossing it holding about 8% of their durable work. A gap that size with an overlap that small is a wish, not a route.
Claims Adjusters, Examiners, and Investigators
Why it looked obvious: It came up as a near neighbour on the work AI is not taking: you already set up appointments for parties to meet for mediation, and their equivalent is to attend mediations or trials. Across both published task lists that is about 7% of the durable work in that job.
Why I am not recommending it: Almost none of it is work you already do: about 7% of the durable side of that job. That is a different job, not a next step.
What I’d stop worrying about
A friend tells you what not to spend fear on. This is that list.
The headline number you read somewhere
The big “X% of jobs” figures are about the whole economy, not about you. The number that describes your job is on this page: 23% of its task weight, across 20 scored tasks. Every row behind it is printed above with the source. Check ours; ignore theirs.
The whole-job doom story
Nothing on this page says this job ends. It says the mix inside it moves. Half the rows above are unchanged or changing shape, and the plan is about which half your week sits in. That is a very different problem, and a solvable one.
Panic-buying a course
Do the twenty-minute sorting exercise first. Most people who buy a course before they have done it buy the wrong one, and the free services listed below will tell you the same thing without charging for it.
The “obvious” next job everyone suggests
I checked the obvious moves and most of them did not survive. The reasons are printed with the routes above, including the pay and the gate. A move that fails on the numbers is worth knowing about so you can stop turning it over at night.
You are reading the United States figures
The United Kingdom splits this work across more than one official group, of which Legal professionals n.e.c. is the closest. The pay and employment figures are not directly comparable, and we do not average them together.
Switch to the United Kingdom page →partial match
In UK official statistics this job is counted as Legal professionals n.e.c.. Pay and employment stay on this page’s own group; the task list and the scores do not cross over.
Your route through this
Where to go next, and what it costs
Free, and complete
The moves above cost nothing. These are the real services that go with them: public, government-funded, and free at the point of use. Nothing on this page is behind an email address or a payment.
Anywhere in the US:
CareerOneStop - Find local training
Search what's running near you, from the Labor Department's own database, before anyone sells you a course.
Free to search; individual programs vary, and some are funded
Anywhere in the US:
An American Job Center will sit down with you for free. Find yours by ZIP code.
Free
Anywhere in the US:
CareerOneStop - Licensed occupations finder
Check what your state actually requires before you pay for anything.
Free
Anywhere in the US:
Free
No Space for this job, but one for what is happening to it
Nothing Collab365 runs is built for arbitrators / mediators / conciliators, and we are not going to point you at the nearest one and call it a fit.
There is one that is not about a job title at all. The AI Authority is about being the person who directs these tools at work rather than the person they get compared to. That is worth saying here, because 23% of the work on this page is already inside what they can do.
7 days free, no card needed. Explore up to 2 Spaces before you choose a plan: you pick a plan later, not now.
The AI Authority is a general community about working with AI, not a course for arbitrators / mediators / conciliators. You do not need it to act on anything here: the moves above cost nothing and stand on their own. The data on this page is the same either way.
Noted, and thank you. We’ll email you if a Space for arbitrators / mediators / conciliators launches. Nothing else.
That did not look like an email address, so nothing was saved. Have another go below.
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No deadline on any of this. The page will still be here, and the data is refreshed on a published schedule rather than when someone wants a headline.
Questions people ask about this job
- Will AI replace Arbitrators, Mediators, and Conciliators?
- Not as a job, but it is already doing parts of the work. Across the 20 official task statements scored for Arbitrators, Mediators, and Conciliators (United States, SOC 23-1022), 23% of the importance-weighted core work is made of tasks today's AI could already do most of. The overall exposure score is 39 out of 100 (range 33–44, band: low). That is a statement about tasks, not about headcount: this measures what AI could do, not whether any employer adopts it, whether the law allows it, or whether doing the routine parts faster creates more demand for the human parts. Figures are from release 2026-q4.1.
- Which tasks in “Arbitrators, Mediators, and Conciliators” can AI already do?
- The highest-scoring tasks in release 2026-q4.1 are: “Prepare settlement agreements for disputants to sign” (88/100, very high); “Set up appointments for parties to meet for mediation” (85/100, very high); “Research laws, regulations, policies, or precedent decisions to prepare for hearings” (72/100, high). Each score comes from five published 0–4 ratings turned into a number by a published formula, and each carries the model's one-sentence reason on the page.
- Which tasks in “Arbitrators, Mediators, and Conciliators” stay human?
- About 55% of this job's task weight sits in work that scores low for AI exposure. The lowest-scoring tasks in release 2026-q4.1 are: “Participate in court proceedings” (3/100, minimal); “Use mediation techniques to facilitate communication between disputants, to further parties' understanding of different perspectives, and to guide parties to…” (7/100, minimal); “Specialize in the negotiation and resolution of environmental conflicts involving issues such as natural resource allocation or regional development planning” (10/100, minimal). Low scores usually mean the task needs a body in a room, a legally accountable human, or trust built in real time. Those are the three things the scoring rubric treats as gates rather than obstacles.
- What should someone working in “Arbitrators, Mediators, and Conciliators” do about AI?
- Start from the ledger rather than the headline: 23% of this job's weighted core work is exposed, and roughly 55% is not. The practical move is to spend more of your week on the tasks that score low, the ones above, and to get fluent at directing AI through the tasks that score high, because those are the parts that change whether or not you are ready for them. This page does not predict your job, and nothing here is career advice tailored to you: the score describes the occupation, not the person.
- How is the AI exposure score for Arbitrators, Mediators, and Conciliators calculated?
- Each official task statement for the occupation is rated on five published 0–4 dimensions (output replicability, physical embodiment, licensed accountability, real-time human trust, and data availability) by claude-opus-5 using scoring prompt task_scoring_v1.0. The model never writes the score; a published formula turns the five ratings into a 0–100 number, so every score can be recomputed by hand. The occupation figure is the importance-weighted mean across 20 scored tasks. The prompt, the rubric, the formula and the full dataset are published at https://futureproof.collab365.com/method and https://futureproof.collab365.com/data/2026-q4.1 under CC BY 4.0.
Where these numbers come from
About the data on this page
- One row is marked low confidence, so treat it as a ballpark rather than a fine measurement.
- The match between this job and its counterpart in the other country is partial, so the two pages count slightly different groups of people.
- 1 of this occupation's scored task statements carry a score that was measured under a different occupation's context, because the statement is shared between jobs and has only been scored once so far. Each one names the occupation it was measured under in the free routes below; none is presented as a measurement for this job.
- Task clusters are not derived in this release, so the task-cluster field is empty and no Collab365 Space routing is attached to this occupation yet.
- Task statements
- onet-dbProcessing: catalogue-bridge → onet-im-rt-weighting → task-scoring → score-aggregation
- Task weights
- onet-db (im-rt)
- Scores
- Rubric task_scoring_v1.0, model claude-opus-5, computed 2026-08-05.
- Pay and employment
- bls-oews (May 2025 estimates (national_M2025_dl.xlsx))bls-oews (May 2025 estimates (national_M2025_dl.xlsx))
Figures on this page come from release 2026-q4.1, published 2026-08-05. Every release keeps its own permanent address, so a figure you cite in March is still there, unchanged, in November.
The plain-English wording on this page is assembled directly from the task statements and the published ratings, not written by hand for this occupation. That is why it is specific, and it is also why we say so.
The routes and free resources further up are today’s, not the release’s (last reviewed 2026-08-05). A route is an offer, not a historical fact, so it moves on its own clock.
Using these figures?
Cite this
Everything on this site is published under CC BY 4.0. Quote it, chart it, sell something built on it. Just say where it came from, and cite the dated release rather than the site, so the figure you quote stays checkable.
Plain text
Collab365 (2026). Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1 (methodVersion 2.0.0, promptVersion task_scoring_v1.0). https://futureproof.collab365.com/data/2026-q4.1. Licensed CC BY 4.0. Built with O*NET data (USDOL/ETA, CC BY 4.0); ONS data (Open Government Licence v3.0); GAISI task framework (arXiv:2507.22748, MIT); BLS data (public domain).
BibTeX
@misc{collab365futureproof2026q41,
title = {Collab365 Futureproof: task-level AI exposure for US and UK occupations, release 2026-q4.1},
author = {{Collab365}},
year = {2026},
url = {https://futureproof.collab365.com/data/2026-q4.1},
note = {Release 2026-q4.1, methodVersion 2.0.0, promptVersion task_scoring_v1.0, CC BY 4.0}
}Data as of release 2026-q4.1, published . Releases never change after publication; when the figures move, a new dated release is published beside this one and this one stays exactly where it is.