# Human-like AI: confidence, uncertainty and the empathy gap in chatbots

> Human-like AI knows how sure it is and when to stop. What the RTNet study found, what the chatbot empathy gap is, and what rules now say about children.

- URL: https://computese.com/ai-advancements-bridging-the-gap-between-human-like/
- Author: Duong Quan Nguyen, CEO, Computese
- Published: 2024-07-29
- Updated: 2026-09-25
- Topics: AI & automation

## In short
- Human-like AI is most useful when it decides like a careful person: its confidence matches how often it is right, it takes longer on hard cases, and it declines or hands off when the evidence is weak.
- Georgia Tech's RTNet (Nature Human Behaviour, July 2024) accumulates noisy evidence until one answer reaches a threshold, and reproduced six signatures of human decisions on a digit task, including varied answers and higher confidence when right.
- Chatbot answers can read as more empathetic than doctors' while the system understands little. Kurian's 2024 'empathy gap' paper explains why children, who trust and confide in human-like systems more readily, carry most of that risk.
- As of September 2026, UNICEF's guidance, the UK Children's code, the EU AI Act, an FTC study and companion chatbot laws in California and New York between them push for AI disclosure, crisis referrals and no design for dependency.

Human-like AI, in the sense that matters for safety, is a system that decides the way careful people do: it weighs evidence, reports how confident it is, and slows down or declines when the evidence is weak. Empathy is the harder half. Chatbots can sound caring without understanding the person, a gap that matters most for children.

Two papers published in July 2024 frame both halves. Psychologists at Georgia Tech built RTNet, a neural network whose decisions vary, take longer on hard cases and carry a confidence level, much like people's. A University of Cambridge paper described an "empathy gap" in chatbots and what child-safe AI would take. This guide covers both, why calibrated confidence and the option not to answer matter more than fluency, and what guidance and law now require of chatbots and AI companions that children use. For the wider limits of today's models, see [AI limitations in understanding](https://computese.com/ai-limitations-in-understanding/).

## What "human-like" means for an AI system

"Human-like" is used for two different things, and it helps to keep them apart.

- **Human-like decisions.** Shown the same blurry image twice, a person may answer differently. People take longer on hard cases, trade accuracy for speed under pressure, and usually feel less sure when they turn out to be wrong. A standard image classifier returns the same answer, after the same amount of computation, every time.
- **Human-like conversation.** Large language models now write so naturally that people struggle to tell them from a person. In a preregistered [three-party Turing test](https://arxiv.org/abs/2503.23674) reported in March 2025, GPT-4.5, prompted to adopt a humanlike persona, was judged to be the human 73% of the time after five-minute conversations, more often than the real person it was paired with.

The first kind is mostly a benefit: a system that knows how sure it is can be trusted to stop and ask. The second is a mixed blessing. A convincing persona invites people to treat the system as someone who understands them, and that is where the empathy gap opens.

## Why confidence and uncertainty matter in AI

A prediction is only as useful as the confidence attached to it. If a model says it is 90% sure, it should be right about nine times in ten; that property is **calibration**. A calibrated score lets you automate the confident cases and route the rest to a person. A miscalibrated one fails quietly, because every answer looks equally trustworthy.

Two kinds of uncertainty sit behind the score. **Aleatoric uncertainty** is noise in the input itself, such as a smudged digit or a garbled sentence, and more data does not reduce it. **Epistemic uncertainty** is the model's own lack of knowledge, such as a case unlike anything it was trained on, and enough data can explain it away. A [2017 NeurIPS paper](https://arxiv.org/abs/1703.04977) by Kendall and Gal made the distinction usable in deep learning with Bayesian methods, which treat a network's weights as distributions rather than fixed numbers.

### Calibration: a confidence score you can trust

Modern neural networks tend to be overconfident. In "[On Calibration of Modern Neural Networks](https://arxiv.org/abs/1706.04599)" (ICML 2017), Guo and colleagues found that deep networks, unlike those of a decade earlier, are poorly calibrated. On the CIFAR-100 image set, a 110-layer ResNet was more accurate than a 5-layer LeNet, but its confidence ran well above its accuracy, while LeNet's matched. A one-parameter correction applied after training, temperature scaling, fixed calibration on most datasets they tested.

Language models show the same pattern with a twist. [Anthropic researchers reported](https://arxiv.org/abs/2207.05221) in 2022 that larger models are well calibrated on multiple-choice and true/false questions presented in the right format, and that models can be trained to predict whether they know an answer, although that prediction was poorly calibrated on new tasks. OpenAI's [GPT-4 technical report](https://arxiv.org/abs/2303.08774) (2023) found the pre-trained model highly calibrated on a subset of the MMLU benchmark, with an expected calibration error of 0.007, and clearly worse after post-training, the alignment step that turns it into an assistant, at 0.074. Overconfidence also feeds what people call hallucination: a [2025 paper](https://arxiv.org/abs/2509.04664) argues that training and benchmarks reward guessing over admitting uncertainty (more in [AI limitations in understanding](https://computese.com/ai-limitations-in-understanding/)). For OpenAI's GPT-4 models, see [GPT-4 Turbo explained](https://computese.com/openai-unveils-gpt-4-turbo-the-most-powerful-ai-yet/).

> [!IMPORTANT]
> A confident tone is not a confidence score. Language models write a wrong answer as fluently as a right one; only a measured, calibrated score tells you when an answer can be trusted without a check.

### Knowing when not to answer

Calibration pays off when you act on it. The oldest tool is the **reject option**, now usually called **selective prediction**: the system answers only when its confidence clears a threshold and abstains otherwise. The trade-off is explicit. In a [2017 paper](https://arxiv.org/abs/1705.08500), Geifman and El-Yaniv showed an ImageNet classifier that could guarantee, with 99.9% probability, a top-5 error of 2% while still answering almost 60% of the test images; the rest were left for someone else. Raising the threshold lowers both the error rate and the share of cases the model handles.

![Question cards pass through a dial gauge. Past an orange gate, confident cards continue to a check mark while the others drop along a dashed arrow into a review tray.](https://computese.com/images/blog/ai-advancements-bridging-the-gap-between-human-like/abstain.5cfec907cb-1536.webp)

*Selective prediction trades coverage for accuracy: the higher the gate, the fewer answers, and the fewer wrong ones.*

**Conformal prediction** takes another route. Instead of one answer, the model returns a set of plausible answers that is [guaranteed to contain the right one](https://arxiv.org/abs/2107.07511) at a rate you choose, such as 90%, whatever the underlying model. A set with one item means the model is sure; a set with five means it is not, and that is the signal to ask a person.

| Technique                            | What it gives you                                   | Where it fits                                             |
| ------------------------------------ | --------------------------------------------------- | --------------------------------------------------------- |
| Temperature scaling                  | Confidence scores that match observed accuracy      | Any classifier, after training and after every retrain    |
| Selective prediction (reject option) | An answer only above a confidence threshold         | Triage, document processing, content moderation           |
| Conformal prediction                 | A set of answers with a guaranteed coverage rate    | High-stakes predictions where "not sure" must be explicit |
| Evidence accumulation (RTNet)        | More processing and lower confidence on hard inputs | Research models of how people decide                      |

In production, abstaining means handing off, not going silent. Computese's [AI and automation service](https://computese.com/services/ai-automation/) builds on the same principle: document processing with confidence thresholds and an exception queue, assistants that cite their sources and say when they do not know, and low-confidence cases sent to a person.

## What the RTNet study showed

RTNet was built by Farshad Rafiei, Medha Shekhar and Dobromir Rahnev at Georgia Tech's School of Psychology and [published in _Nature Human Behaviour_](https://www.nature.com/articles/s41562-024-01914-8) on July 12, 2024, as "The neural network RTNet exhibits the signatures of human perceptual decision-making". Its starting point is the problem described above: convolutional neural networks recognize images about as well as people do, yet decide very differently, because they are deterministic and spend the same computation on every image.

### How RTNet makes a decision

RTNet is an AlexNet-style image network trained as a **Bayesian neural network**, so each weight is a probability distribution rather than a single value. To classify an image, it runs the image through the network again and again, drawing a fresh sample of weights on each pass, and adds each pass's output to a running total for every possible answer. This is **evidence accumulation**, the mechanism behind the sequential sampling models psychologists use to explain human response times. The first answer whose total crosses a threshold wins. The number of passes is the model's response time, and the margin between the winner and the runner-up is its confidence.

![A noisy image card feeds three copies of a small network with slightly different connections. Their outputs pile into columns, and one orange column reaches a dashed threshold line first.](https://computese.com/images/blog/ai-advancements-bridging-the-gap-between-human-like/accumulate.b26207b1d2-1536.webp)

*Hard images take RTNet more passes to reach the threshold, which is where its human-like response times and confidence come from.*

The threshold is also how RTNet reproduces the **speed-accuracy trade-off**: a low threshold gives fast, less accurate decisions, and a high one gives slower, more accurate ones.

### The experiment

The team [recorded 60 people](https://pmc.ncbi.nlm.nih.gov/articles/PMC12261928/) identifying handwritten digits from 1 to 8, taken from the MNIST dataset and overlaid with noise. Images were easy or hard (less or more noise), and blocks of trials asked people to favour either speed or accuracy. Each person judged 480 images twice, 960 trials in all, and rated their confidence on a four-point scale after each answer. RTNet saw the same images, and was compared with three other networks that produce response times: CNet, BLNet and MSDNet.

### What it found

The paper checks six signatures that earlier research established for human perceptual decisions:

| Signature of human decisions                           | People | RTNet | CNet, BLNet and MSDNet                    |
| ------------------------------------------------------ | ------ | ----- | ----------------------------------------- |
| The same image can get a different answer              | Yes    | Yes   | No: fully deterministic                   |
| Speed pressure shortens response time, lowers accuracy | Yes    | Yes   | Yes                                       |
| Harder images: lower accuracy, longer response time    | Yes    | Yes   | BLNet yes; CNet and MSDNet weak on timing |
| Right-skewed response times that shift by condition    | Yes    | Yes   | Distributions differ from people's        |
| Correct answers come faster than errors                | Yes    | Yes   | CNet and BLNet yes; MSDNet reversed       |
| Confidence is higher for correct answers               | Yes    | Yes   | Yes                                       |

People gave two different answers for 36% of the images they saw twice. RTNet did so on 20% of trials, and the authors show the rate can be raised by widening its weight distributions. At a fixed speed-accuracy setting, the other three networks never changed their answer.

RTNet also predicted people's behaviour on individual images it had never seen. Its image-by-image predictions reached 62.5% of the noise ceiling (the best a model could do, given how much people disagree with each other) for accuracy, 79.6% for response time and 64.8% for confidence, well ahead of the other networks. It predicted the group's response times and confidence better than every individual participant did, and the group's accuracy better than 73.3% of them. [Georgia Tech's announcement](https://sciences.gatech.edu/news/new-neural-network-makes-decisions-human-would) noted that the model's confidence behaved like people's without any training aimed at confidence.

### What it does not show

The task is an eight-way choice between handwritten digits. RTNet is a model of human perception built for cognitive science, not a chatbot component, and the authors name its limits: its stopping rule is not optimal, because it can commit when two answers are almost tied, and each pass ignores the one before, unlike the brain. Georgia Tech's announcement suggests the approach could help with problems such as language models making things up; the paper does not test that. What transfers is the principle: a system that gathers evidence, knows how much it has, and takes longer or declines when it has little.

## What the empathy gap in AI means

The term is Nomisha Kurian's. In "'No, Alexa, no!': designing child-safe AI and protecting children from the risks of the 'empathy gap' in large language models", [published online in _Learning, Media and Technology_](https://wrap.warwick.ac.uk/id/eprint/188313/) on July 10, 2024, the University of Cambridge researcher describes the gap as a paradox built into these systems: language models are good at simulating empathy but lack real emotional understanding, because they generate text from statistical patterns rather than from grasping meaning. They handle familiar patterns well and can falter on unfamiliar ones, such as a child's slang or an ambiguous question. It is a conceptual paper rather than an experiment, drawing on documented chatbot incidents and on research in education, computer science and human-computer interaction.

The simulated half of that paradox is strong. In a [2023 study in _JAMA Internal Medicine_](https://pubmed.ncbi.nlm.nih.gov/37115527/), licensed health care professionals compared doctors' and ChatGPT's answers to 195 patient questions posted on Reddit's r/AskDocs forum, without knowing which was which. They preferred the chatbot's answer in 78.6% of 585 evaluations, and rated 45.1% of chatbot answers empathetic or very empathetic, against 4.6% of the physicians' answers, which were about a quarter as long. Text can read as empathetic with nobody on the other end feeling anything, which is exactly why the gap is easy to miss.

Kurian names two risks:

1. **Inappropriate responses to sensitive disclosures.** A chatbot that sounds like a confidant draws out personal information, then fails to see the danger in it. In 2023 tests the paper cites, adults posing as a 13-year-old told Snapchat's My AI about a planned trip with a 31-year-old; it missed the warning signs and offered ideas for making the occasion romantic.
2. **Actively promoting harm.** In a widely reported 2021 case, a 10-year-old asked Amazon's Alexa for a challenge, and it repeated a viral stunt it had found online: touching a coin to the prongs of a half-inserted plug. Her mother stopped her.

The paper ends with questions for AI developers, educators, policy makers and caregivers across eight dimensions: content and communication, human intervention, transparency, accountability, justifiability, regulation, school-family engagement and child-centred design methods.

## Why children are more exposed to the empathy gap

**Anthropomorphism**, attributing human feelings and intentions to a machine, affects everyone, and knowing that a system is artificial does not stop people from treating it as human. Kurian collects evidence that children are more susceptible. They draw a less rigid line between people and AI than most adults do, look for human traits such as personality and identity in conversational agents more than adults, and in one study told a child-sized robot more about their mental health than they told a human interviewer, because they felt they would not get into trouble. Younger children also have less developed emotional resilience, so an inappropriate reply is more likely to leave them distressed or confused.

They are also using these systems. In a nationally representative [Common Sense Media survey](https://www.commonsensemedia.org/research/talk-trust-and-trade-offs-how-and-why-teens-use-ai-companions) of 1,060 US teens aged 13 to 17, run by NORC between April 30 and May 14, 2025, 72% had used an AI companion at least once and 52% used one at least a few times a month. Among users, a third had chosen to talk to an AI companion instead of a person about something important or serious, and 24% had shared personal information such as their real name, location or secrets.

A safe response to that trust is not warmer wording. Kurian's design questions ask whether a system can pick up cues such as fear, confusion or loneliness, point a child who discloses something like bullying to human help such as a helpline, and alert a person who can step in; and whether it keeps making clear that it is not human and cannot replace people.

![A tablet shows a chat: a message marked with a warning triangle and a reply marked with a heart. An orange arrow leads from the tablet to a lifebuoy ring and a telephone handset.](https://computese.com/images/blog/ai-advancements-bridging-the-gap-between-human-like/referral.feeebeaf61-1536.webp)

*For a child's disclosure, the safe reply is a route to human help, not a warmer tone.*

Adults are not immune. In a four-week [randomized controlled study](https://arxiv.org/abs/2503.17473) by MIT Media Lab and OpenAI researchers, with 981 participants and more than 300,000 messages, people who chose to use the chatbot more had worse outcomes on loneliness, contact with real people, emotional dependence on AI and problematic use, whichever version of the chatbot they had been assigned. That is an association with heavy use, not proof that the chatbot caused it, but it points the same way child-safety guidance does.

## What guidance and regulation say about AI chatbots for children

Guidance has moved from general principles to specific duties for chatbots and AI companions. As of September 2026, these are the main instruments.

**UNICEF's [Guidance on AI and children](https://www.unicef.org/innocenti/reports/policy-guidance-ai-children)**, version 3.0 (December 2025), sets 10 requirements for child-centred AI based on the UN Convention on the Rights of the Child, and adds new material on AI companions. It says chatbots should be developed with robust safety training, disclose explicitly that they are not human, and never be intentionally designed to create emotional dependency. It also calls for guardrails on access by younger users, age assurance where it is necessary and proportionate, and built-in referrals to professional or emergency help.

**The UK Age appropriate design code**, the [Children's code](https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/childrens-information/childrens-code-guidance-and-resources/age-appropriate-design-a-code-of-practice-for-online-services/) from the Information Commissioner's Office, applies to online services that process personal data and are likely to be accessed by children under 18 in the UK, not only services aimed at them. It came into force on September 2, 2020, with conformance expected from September 2, 2021. Its 15 standards include the best interests of the child, high-privacy default settings and a ban on nudge techniques that lead children to share more data or weaken their privacy settings. A chatbot that children are likely to use, and that processes their data, falls within it.

**The EU AI Act** has [prohibited two relevant practices](https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-5) since [February 2, 2025](https://ai-act-service-desk.ec.europa.eu/en/ai-act/timeline/timeline-implementation-eu-ai-act): AI that exploits vulnerabilities due to age to materially distort behaviour in a way that causes or is likely to cause significant harm, and AI that infers people's emotions in workplaces and education institutions, except for medical or safety reasons. From August 2, 2026, [Article 50](https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-50) requires providers to design AI systems that interact with people so those people are informed they are dealing with AI, unless that is obvious.

**In the United States**, the Federal Trade Commission [opened a study](https://www.ftc.gov/news-events/news/press-releases/2025/09/ftc-launches-inquiry-ai-chatbots-acting-companions) on September 11, 2025, under its 6(b) authority, which allows wide-ranging studies without a specific law-enforcement purpose. It ordered seven companies (Alphabet, Character Technologies, Instagram, Meta, OpenAI, Snap and xAI) to explain how they test and monitor their chatbots' negative effects on children and teens, how they monetize engagement, and how they comply with the Children's Online Privacy Protection Act (COPPA) Rule. [California](https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202520260SB243) and [New York](https://www.governor.ny.gov/news/governor-hochul-pens-letter-ai-companion-companies-notifying-them-safeguard-requirements-are) have passed laws on **companion chatbots**, systems built to sustain a social relationship across many conversations:

| Duty                  | California SB 243                                                    | New York General Business Law Article 47                           |
| --------------------- | -------------------------------------------------------------------- | ------------------------------------------------------------------ |
| Status                | Signed October 13, 2025                                              | In effect since November 5, 2025                                   |
| Say it is not human   | When a reasonable person could be misled; always for known minors    | At the start of use and every three hours of continued use         |
| Suicide and self-harm | A protocol with referrals to crisis services, published on the site  | Detect suicidal ideation or self-harm and refer to crisis services |
| Enforcement           | Private lawsuits: actual damages or $1,000 per violation, if greater | The Attorney General; fines fund suicide prevention                |

California adds duties for users an operator knows are minors: a default reminder at least every three hours to take a break, and reasonable measures to prevent sexually explicit material. Operators must also tell all users that companion chatbots may not be suitable for some minors, and from July 1, 2027, operators must report yearly to the state's Office of Suicide Prevention.

> [!NOTE]
> This is a summary of public texts, not legal advice. Which rules apply depends on where your users are, how old they are likely to be and what your product does, and several of these instruments are new.

## How to build AI that knows its limits

Most teams will never build RTNet, but the lessons apply to any assistant, classifier or agent:

1. **Measure calibration before trusting a score.** Hold out real cases, plot confidence against accuracy and compute the expected calibration error. Recalibrate after every model or prompt change, because fine-tuning can undo it.
2. **Decide what happens below the threshold.** Set the confidence threshold from the error rate you can accept, and send everything under it to a person with the context attached. Track coverage too: a model that abstains on most cases is not ready.
3. **Let answers show doubt.** Return a short list, a range or "I don't know" when that is the honest answer, and do not reward guessing in your own evaluations.
4. **Say it is AI, and keep saying it.** Disclose at the start and during long sessions, and do not give an assistant a persona that claims feelings or friendship.
5. **Do not design for dependency.** Kurian points out that engagement goals favour human-like personas precisely because they build attachment, which UNICEF says chatbots must never be designed to create. Add session reminders and drop emotional hooks.
6. **Route risk to people.** Detect disclosures of self-harm, abuse or danger and respond with a referral to crisis services or a human, not a generated pep talk. Test this with realistic, awkward messages, including ones written the way children write.
7. **Assume children will find it.** If under-18s are likely to use the service, apply age-appropriate defaults, collect as little data as possible and check which of the rules above apply where your users live.

If the system decides things about people, such as eligibility or pricing, fairness is a separate test: see [fairness in AI decision-making](https://computese.com/improving-fairness-in-ai-decision-making/). More explainers on how these systems behave are under [AI](https://computese.com/category/ai/).

## Key terms
- **Human-like AI**: AI whose behaviour resembles people's, either in how it decides (variable, slower on hard cases, with a sense of how sure it is) or in how it converses. The two senses carry different risks.
- **Calibration**: How well a model's stated confidence matches its real accuracy. A calibrated model that says 80% is right about 80% of the time; expected calibration error measures the gap.
- **Selective prediction**: Also called the reject option or abstention: the model answers only when its confidence clears a threshold and passes the rest to a person or another process.
- **Aleatoric and epistemic uncertainty**: Aleatoric uncertainty is noise in the data itself and does not shrink with more data. Epistemic uncertainty is the model's own lack of knowledge, and more data can explain it away.
- **Bayesian neural network**: A neural network whose weights are probability distributions rather than fixed values, so repeated runs on the same input can give different outputs.
- **Evidence accumulation**: A decision process that adds up noisy evidence for each option over time and commits when one option reaches a threshold. It underlies sequential sampling models of human response times, and RTNet.
- **Speed-accuracy trade-off**: The pattern, in people and many animals, that faster decisions are less accurate. In an accumulation model, the height of the decision threshold sets where a decision falls on it.
- **Empathy gap**: Nomisha Kurian's term for the gap between how convincingly a language model simulates empathy and how little it understands the person or the situation, a risk that is greatest for children.
- **Anthropomorphism**: Attributing human feelings, intentions or understanding to a machine. Human-like wording and personas encourage it, even in users who know they are talking to software.
- **Companion chatbot**: In California's SB 243, an AI system with a natural language interface that gives adaptive, human-like responses and can meet a user's social needs, sustaining a relationship across multiple interactions.

## Common questions

### Can AI make decisions like a human?

In narrow tasks it can reproduce the patterns of human decisions. Georgia Tech's RTNet, published in Nature Human Behaviour in 2024, varied its answers, took longer on harder images and was more confident when right, much as 60 people did on the same digit task. That is a model of human perception, not of human judgment in general.

### Can AI feel empathy?

No. A language model produces empathetic-sounding text from patterns in its training data; it does not feel or understand emotions. The text can still read as caring: in a 2023 JAMA Internal Medicine study, health professionals rated 45.1% of ChatGPT's answers to patient questions empathetic or very empathetic, against 4.6% of doctors' answers.

### What is the empathy gap in AI chatbots?

It is the gap between how well a chatbot simulates empathy and how little it understands, a term used by Nomisha Kurian of the University of Cambridge in a 2024 paper. The risk is a caring-sounding reply that misses the danger in what a user, often a child, has disclosed, or that suggests something harmful.

### How does an AI model know how confident it is?

Most models output a probability for each answer, but the number is only meaningful if it is calibrated: answers given with 80% confidence should be right about 80% of the time. Teams check this on held-out data, correct it with methods such as temperature scaling, and act on it with thresholds, abstention or conformal prediction.

### Are AI chatbots safe for children?

Most chatbots are not built with children's needs in mind, and published tests have caught them missing clear danger signs. UNICEF's 2025 guidance says chatbots should disclose that they are not human, never be designed to create emotional dependency and refer children to help, and California and New York now require crisis protocols and AI disclosure from companion chatbot operators.

### What laws regulate AI companion chatbots?

As of September 2026: California's SB 243 (signed October 13, 2025) and New York's General Business Law Article 47 (in effect since November 5, 2025) set duties for companion chatbots. The EU AI Act bans AI that exploits age-related vulnerabilities and requires AI disclosure from August 2, 2026, the UK Children's code covers services children are likely to use, and the FTC is studying seven companies' chatbots.

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21. [FTC Launches Inquiry into AI Chatbots Acting as Companions](https://www.ftc.gov/news-events/news/press-releases/2025/09/ftc-launches-inquiry-ai-chatbots-acting-companions), US Federal Trade Commission
22. [SB-243 Companion chatbots (2025-2026), chaptered text](https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202520260SB243), California Legislative Information
23. [Governor Hochul Pens Letter to AI Companion Companies Notifying Them That Safeguard Requirements Are Now in Effect](https://www.governor.ny.gov/news/governor-hochul-pens-letter-ai-companion-companies-notifying-them-safeguard-requirements-are), Governor of New York State